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Pseudorapidity (eta) distributions of charged particles produced in proton-proton collisions at a centre-of-mass energy of 8 TeV are measured in the ranges abs(eta) < 2.2 and 5.3 < abs(eta) < 6.4 covered by the CMS and TOTEM detectors, respectively. The data correspond to an integrated luminosity of 45 inverse microbarns. Measurements are presented for three event categories. The most inclusive category is sensitive to 91-96% of the total inelastic proton-proton cross section. The other two categories are disjoint subsets of the inclusive sample that are either enhanced or depleted in single diffractive dissociation events. The data are compared to models used to describe high-energy hadronic interactions. None of the models considered provide a consistent description of the measured distributions.
Charged particle multiplicity (Inclusive) -- syst -- BB: bin-by-bin uncertainty.
Charged particle multiplicity (Non Single Diffractive dissociation (NSD) enhanced) -- syst -- BB: bin-by-bin uncertainty.
Charged particle multiplicity (Single Diffractive dissociation (SD) enhanced) -- syst -- BB: bin-by-bin uncertainty.
Results are presented from a search for CP violation in top quark pair production, using proton-proton collisions at a center-of-mass energy of 13 TeV. The data used for this analysis consist of final states with two charged leptons collected by the CMS experiment, and correspond to an integrated luminosity of 35.9 fb$^{-1}$. The search uses two observables, $\mathcal{O}_1$ and $\mathcal{O}_3$, which are Lorentz scalars. The observable $\mathcal{O}_1$ is constructed from the four-momenta of the charged leptons and the reconstructed top quarks, while $\mathcal{O}_3$ consists of the four-momenta of the charged leptons and the b quarks originating from the top quarks. Asymmetries in these observables are sensitive to CP violation, and their measurement is used to determine the chromoelectric dipole moment of the top quark. The results are consistent with the expectation from the standard model.
Measured asymmetries of O_1 and O_3 with statistical uncertainties
The measured asymmetries of O_1 and O_3, and dimensionless CEDM \ImdtG, extracted using the asymmetries in O_1 and O_3, with their uncertainties.
Results for the covariance matrix where the parameters a and b are taken from a linear fit (equation 11) to the different CP-violating samples (CEMD).
A search for physics beyond the standard model (SM) in the final state with a hadronically decaying tau lepton and a neutrino is presented. This analysis is based on data recorded by the CMS experiment from proton-proton collisions at a center-of-mass energy of 13 TeV at the LHC, corresponding to a total integrated luminosity of 138 fb$^{=1}$. The transverse mass spectrum is analyzed for the presence of new physics. No significant deviation from the SM prediction is observed. Limits are set on the production cross section of a W' boson decaying into a tau lepton and a neutrino. Lower limits are set on the mass of the sequential SM-like heavy charged vector boson and the mass of a quantum black hole. Upper limits are placed on the couplings of a new boson to the SM fermions. Constraints are put on a nonuniversal gauge interaction model and an effective field theory model. For the first time, upper limits on the cross section of $t$-channel leptoquark (LQ) exchange are presented. These limits are translated into exclusion limits on the LQ mass and on its coupling in the $t$-channel. The sensitivity of this analysis extends into the parameter space of LQ models that attempt to explain the anomalies observed in B meson decays. The limits presented for the various interpretations are the most stringent to date. Additionally, a model-independent limit is provided.
The transverse mass distribution of $ au$ leptons and missing transverse momentum observed in the Run-2 data (black dots with statistical uncertainty) as well as the expectation from SM processes (stacked histograms). Different signal hypotheses normalized to 10 fb$^{-1}$ are illustrated as dashed lines for exemplary SSM W$\prime$ boson, QBH and EFT signal hypotheses. The ratios of the background-subtracted data yields to the expected background yields are presented in the lower panel. The combined statistical and systematic uncertainties in the background are represented by the grey shaded band in the ratio panel.
Bayesian upper exclusion limits at 95% CL on the product of the cross section and branching fraction of a W$\prime$ boson decaying to a $\tau$ lepton and a neutrino in the SSM model. For this model, W$\prime$ boson masses of up to 4.8 TeV can be excluded. The limit is given by the intersection of the observed (solid) limit and the theoretical cross section (blue dotted curve). The 68 and 95% quantiles of the limits are represented by the green and yellow bands, respectively. The $\sigma \mathcal{B}$ for an SSM W' boson, along with its associated uncertainty, calculated at NNLO precision in QCD is shown.
Bayesian 95% CL model-independent upper limit on the product of signal cross sections and branching fraction for the $\tau+\nu$ decay for a back-to-back $\tau$ lepton plus $p_{T}^{miss}$ topology. To calculate this limit, all events for signal, background, and data are summed starting from a minimum $m_{T}$ threshold and then divided by the total number of events. No assumption on signal shape is included in this limit. The expected (dashed line) and observed (solid line) limits are shown as well as the 68% and 95% CL uncertainty bands (green and yellow, respectively).
Bayesian upper exclusion limits on the ratio $g_{W'}/g_{W}$ for an SSM-like W$\prime$ boson are shown. The unity coupling ratio (blue dotted curve) corresponds to the SSM common benchmark. The 68 and 95% quantiles of the limits are represented by the green and yellow bands, respectively.
Bayesian lower exclusion limits on the NUGIM G(221) mixing angle $\cot(\theta_{E})$ are shown as a function of the W$\prime$ boson mass. The theoretically excluded region is shaded in grey. The 68 and 95% quantiles of the limits are represented by the green and yellow bands, respectively.
Bayesian upper exclusion limits at 95% CL on the product of the production cross section and branching fraction of a QBH in an associated $ au$ lepton and neutrino final state. Minimum threshold masses $m_{th}$ of up to 6.6 TeV are excluded at 95% CL. The observed limit (solid line) is obtained from the intersection with the LO QBH cross section (blue dotted curve). The 68 and 95% quantiles of the limits are represented by the green and yellow bands, respectively.
Bayesian upper limits at 95% CL on the cross section of the process $pp\rightarrow\tau\nu$ mediated via LQ exchange in the t-channel. The inner (green) band and the outer (yellow) band indicate the regions containing 68 and 95%, respectively, of the distribution of limits expected under the background-only hypothesis. The predicted LQ cross section at LO in the three coupling benchmark scenarios is depicted in different colors for $g_{U}=1$. The uncertainty bandy correspond to the sum in quadrature of PDF and scale variations. The first benchmark scenario considers only couplings to left-handed SM fermions (i.e. $\beta_{\text{R}}^{ij}=0$) and is referred to as "best fit LH". The second benchmark, referred to as "best fit LH+RH", considers $|\beta_{\text{R}}^{\text{b}\tau}|=1$ and all other $\beta_{\text{R}}^{ij}=0$. In the third "democratic" benchmark, equal couplings only to LH fermions are assumed, i.e. $\beta_{\text{L}}^{ij}=1$ and $\beta_{\text{R}}^{ij}=0$ for all $i$ and $j$.
Expected and observed lower limits of the LQ mass as a function of the coupling $g_{U}$ in the LH scenario. The blue band shows the 68% and 95% regions of $g_{U}$ preferred by the fit to the b anomalies data.
Expected and observed lower limits of the LQ mass as a function of the coupling $g_{U}$ in the LH+RH scenario. The blue band shows the 68% and 95% regions of $g_{U}$ preferred by the fit to the b anomalies data.
Expected and observed lower limits of the LQ mass as a function of the coupling $g_{U}$ in the democratic scenario. The blue band shows the 68% and 95% regions of $g_{U}$ preferred by the fit to the b anomalies data.
Bayesian upper exclusion limits at 95% CL on each of the Wilson coefficients described by the EFT model based on 2016-2018 data. The three different coupling types represent a left-handed vector coupling ($\epsilon^{cb}_{L}$), tensor-like coupling ($\epsilon^{cb}_{T}$), and scalar-tensor-like coupling ($\epsilon^{cb}_{S_{L}}$). The 68 and 95% quantiles of the limits are represented by the green and yellow bands, respectively.
Summary of exclusion limits (expected and observed) calculated at 95% CL for full Run-2 CMS data.
Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "year_binnumber", following the binning from Figure 4.
Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "year_binnumber", following the binning used in Figure 4.
Predicted signal yields for the 2017 data-taking period, corresponding to 41.3 fb$^{-1}$, after the application of each search requirement (cumulative) for various signal hypothesis. The requirements listed are presented as total efficiencies w.r.t. the previous selection step.
Three searches are presented for signatures of physics beyond the standard model (SM) in $\tau\tau$ final states in proton-proton collisions at the LHC, using a data sample collected with the CMS detector at $\sqrt{s}$ = 13 TeV, corresponding to an integrated luminosity of 138 fb$^{-1}$. Upper limits at 95% confidence level (CL) are set on the products of the branching fraction for the decay into $\tau$ leptons and the cross sections for the production of a new boson $\phi$, in addition to the H(125) boson, via gluon fusion (gg$\phi$) or in association with b quarks, ranging from $\mathcal{O}$(10 pb) for a mass of 60 GeV to 0.3 fb for a mass of 3.5 TeV each. The data reveal two excesses for gg$\phi$ production with local $p$-values equivalent to about three standard deviations at $m_\phi$ = 0.1 and 1.2 TeV. In a search for $t$-channel exchange of a vector leptoquark U$_1$, 95% CL upper limits are set on the dimensionless U$_1$ leptoquark coupling to quarks and $\tau$ leptons ranging from 1 for a mass of 1 TeV to 6 for a mass of 5 TeV, depending on the scenario. In the interpretations of the $M_\mathrm{h}^{125}$ and $M_\mathrm{h, EFT}^{125}$ minimal supersymmetric SM benchmark scenarios, additional Higgs bosons with masses below 350 GeV are excluded at 95% CL.
Expected and observed $95\%\text{ CL}$ upper limits on the product of the cross sections and branching fraction for the decay into $\tau$ leptons for $gg\phi$ production in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$, in addition to $\text{H}(125)$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. In this case, $bb\phi$ production rate has been profiled. The peak in the expected $gg\phi$ limit is tribute to a loss of sensitivity around $90\text{ GeV}$ due to the background from $Z/\gamma^\ast\rightarrow\tau\tau$ events. Numerical values provided in this table correspond to Figure 10a of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on the product of the cross sections and branching fraction for the decay into $\tau$ leptons for $bb\phi$ production in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$, in addition to $\text{H}(125)$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. In this case, $gg\phi$ production rate has been profiled. Numerical values provided in this table correspond to Figure 10b of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on the product of the cross sections and branching fraction for the decay into $\tau$ leptons for $gg\phi$ production in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$, in addition to $\text{H}(125)$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. In this case, $bb\phi$ production rate has been fixed to zero. Numerical values provided in this table correspond to Figure 37 of the auxilliary material of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on the product of the cross sections and branching fraction for the decay into $\tau$ leptons for $bb\phi$ production in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$, in addition to $\text{H}(125)$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. In this case, $gg\phi$ production rate has been fixed to zero. Numerical values provided in this table correspond to Figure 38 of the auxilliary material of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on the product of the cross sections and branching fraction for the decay into $\tau$ leptons for $gg\phi$ production in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$, in addition to $\text{H}(125)$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. In this case, $bb\phi$ production rate has been profiled and only top quarks have been considered in the $gg\phi$ loop. Numerical values provided in this table correspond to Figure 39 of the auxilliary material of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on the product of the cross sections and branching fraction for the decay into $\tau$ leptons for $gg\phi$ production in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$, in addition to $\text{H}(125)$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. In this case, $bb\phi$ production rate has been profiled and only bottom quarks have been considered in the $gg\phi$ loop. Numerical values provided in this table correspond to Figure 40 of the auxilliary material of the publication.
Local significance for a $gg\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $bb\phi$ production rate has been profiled. Numerical values provided in this table correspond to Figure 31 of the auxilliary material of the publication.
Local significance for a $bb\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $gg\phi$ production rate has been profiled. Numerical values provided in this table correspond to Figure 32 of the auxilliary material of the publication.
Local significance for a $gg\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $bb\phi$ production rate has been fixed to zero. Numerical values provided in this table correspond to Figure 33 of the auxilliary material of the publication.
Local significance for a $bb\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $gg\phi$ production rate has been fixed to zero. Numerical values provided in this table correspond to Figure 34 of the auxilliary material of the publication.
Local significance for a $gg\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $bb\phi$ production rate has been profiled and only top quarks have been considered in the $gg\phi$ loop. Numerical values provided in this table correspond to Figure 35 of the auxilliary material of the publication.
Local significance for a $gg\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $bb\phi$ production rate has been profiled and only bottom quarks have been considered in the $gg\phi$ loop. Numerical values provided in this table correspond to Figure 36 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $95\text{ GeV}$, produced via gluon-fusion ($gg\phi$), via vector boson fusion ($qq\phi$) or in association with b quarks ($bb\phi$). In this case, $bb\phi$ production rate is profiled, whereas the scan is performed in the $gg\phi$ and $qq\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 64 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $60\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 65 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $60\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 66 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $80\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 67 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $80\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 68 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $95\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 69 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $95\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 70 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $100\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 71 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $100\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 72 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $120\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 73 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $120\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 74 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $125\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 75 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $125\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 76 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $130\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 77 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $130\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 78 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $140\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 79 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $140\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 80 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $160\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 81 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $160\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 82 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $180\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 83 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $180\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 84 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $200\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 85 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $200\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 86 of the auxilliary material of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on $g_U$ in the VLQ BM 1 scenario in a mass range of $1\leq m_U\leq 5\text{ TeV}$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. Numerical values provided in this table correspond to Figure 12a of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on $g_U$ in the VLQ BM 2 scenario in a mass range of $1\leq m_U\leq 5\text{ TeV}$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. Numerical values provided in this table correspond to Figure 12b of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on $g_U$ in the VLQ BM 3 scenario in a mass range of $1\leq m_U\leq 5\text{ TeV}$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. Numerical values provided in this table correspond to Figure 92 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $60\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11a of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $80\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 41 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $95\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 42 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $100\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11b of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $120\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 43 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $125\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11c of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $130\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 44 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $140\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 45 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $160\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11d of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $180\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 46 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 47 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $250\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11e of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $300\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 48 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $350\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 49 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $400\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 50 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $450\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 51 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $500\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11f of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 52 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $700\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 53 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $800\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 54 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $900\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 55 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1000\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11g of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11h of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1400\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 56 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 57 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1800\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 58 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2000\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 59 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2300\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 60 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 61 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2900\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 62 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $3200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 63 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $3500\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11i of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $60\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11a of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $80\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 41 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $95\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 42 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $100\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11b of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $120\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 43 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $125\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11c of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $130\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 44 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $140\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 45 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $160\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11d of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $180\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 46 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 47 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $250\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11e of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $300\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 48 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $350\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 49 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $400\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 50 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $450\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 51 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $500\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11f of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 52 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $700\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 53 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $800\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 54 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $900\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 55 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1000\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11g of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11h of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1400\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 56 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 57 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1800\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 58 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2000\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 59 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2300\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 60 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 61 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2900\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 62 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $3200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 63 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $3500\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11i of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 1\text{ TeV}$, in the VLQ BM 1 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 99 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 2\text{ TeV}$, in the VLQ BM 1 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 100 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 3\text{ TeV}$, in the VLQ BM 1 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 101 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 4\text{ TeV}$, in the VLQ BM 1 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 102 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 5\text{ TeV}$, in the VLQ BM 1 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 103 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 1\text{ TeV}$, in the VLQ BM 2 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 104 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 2\text{ TeV}$, in the VLQ BM 2 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 105 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 3\text{ TeV}$, in the VLQ BM 2 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 106 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 4\text{ TeV}$, in the VLQ BM 2 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 107 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 5\text{ TeV}$, in the VLQ BM 2 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 108 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 1\text{ TeV}$, in the VLQ BM 3 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 109 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 2\text{ TeV}$, in the VLQ BM 3 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 110 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 3\text{ TeV}$, in the VLQ BM 3 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 111 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 4\text{ TeV}$, in the VLQ BM 3 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 112 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 5\text{ TeV}$, in the VLQ BM 3 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 113 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 13a of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 13a of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ quantile contour of Figure 13a of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ quantile contour of Figure 13a of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ quantile contour of Figure 13a of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ quantile contour of Figure 13a of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 13b of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 13b of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ quantile contour of Figure 13b of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ quantile contour of Figure 13b of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ quantile contour of Figure 13b of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ quantile contour of Figure 13b of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 114 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 114 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 114 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 114 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 114 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 114 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 115 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 115 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 115 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 115 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 115 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 115 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 116 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 116 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 116 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 116 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 116 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 116 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 117 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 117 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 117 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 117 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 117 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 117 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 118 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 118 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 118 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 118 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 118 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 118 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 119 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 119 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 119 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 119 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 119 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 119 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario. Numerical values provided in this table correspond to the observed contour of Figure 120 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 120 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 120 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 120 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 120 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 120 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 122 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 122 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 122 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 122 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 122 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 122 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 123 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 123 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 123 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 123 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 123 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 123 of the auxilliary material of the publication.
Fractions of the cross-section $\sigma(gg\phi)$ as expected from SM for the loop contributions with only top quarks, only bottom quarks and from the top-bottom interference. These values are used to scale the corresponding signal components for a given mass $m_\phi$.
Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for high-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for high-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for high-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 25 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 25 of the auxilliary material of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 25 of the auxilliary material of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8a of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8a of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8a of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 26 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 26 of the auxilliary material of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 26 of the auxilliary material of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8b of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8b of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8b of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 27 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 27 of the auxilliary material of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 27 of the auxilliary material of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 28 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 28 of the auxilliary material of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 28 of the auxilliary material of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8e of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8e of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8e of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8f of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8f of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8f of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for low-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for low-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for low-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 21 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 21 of the auxilliary material of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 21 of the auxilliary material of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 22 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 22 of the auxilliary material of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 22 of the auxilliary material of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 23 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 23 of the auxilliary material of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 23 of the auxilliary material of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 24 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 24 of the auxilliary material of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 24 of the auxilliary material of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 20 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 20 of the auxilliary material of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 20 of the auxilliary material of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
An inclusive search for long-lived exotic particles decaying to a pair of muons is presented. The search uses data collected by the CMS experiment at the CERN LHC in proton-proton collisions at $\sqrt{s}$ = 13 TeV in 2016 and 2018 and corresponding to an integrated luminosity of 97.6 fb$^{-1}$. The experimental signature is a pair of oppositely charged muons originating from a common secondary vertex spatially separated from the pp interaction point by distances ranging from several hundred $\mu$m to several meters. The results are interpreted in the frameworks of the hidden Abelian Higgs model, in which the Higgs boson decays to a pair of long-lived dark photons Z$_\mathrm{D}$, and of a simplified model, in which long-lived particles are produced in decays of an exotic heavy neutral scalar boson. For the hidden Abelian Higgs model with $m_\mathrm{Z_D}$ greater than 20 GeV and less than half the mass of the Higgs boson, they provide the best limits to date on the branching fraction of the Higgs boson to dark photons for $c\tau$(Z$_\mathrm{D}$) (varying with $m_\mathrm{Z_D}$) between 0.03 and ${\approx}$ 0.5 mm, and above ${\approx}$ 0.5 m. Our results also yield the best constraints on long-lived particles with masses larger than 10 GeV produced in decays of an exotic scalar boson heavier than the Higgs boson and decaying to a pair of muons.
Level-1 muon trigger efficiency in cosmic-ray muon data (blue) and signal simulation (red) as a function of $d_0$, for the Level-1 trigger $p_T$ threshold used in the 2016 analysis triggers. The denominator in the efficiency calculation is the number of STA muons with $|\eta| < 1.2$ and $p_T > 33$ GeV.
Level-1 muon trigger efficiency in cosmic-ray muon data (blue) and signal simulation (red) as a function of $d_0$, for the Level-1 trigger $p_T$ threshold used in the 2016 analysis triggers. The denominator in the efficiency calculation is the number of STA muons with $|\eta| < 1.2$ and $p_T > 33$ GeV.
Level-1 muon trigger efficiency in cosmic-ray muon data (blue) and signal simulation (red) as a function of $d_0$, for the Level-1 trigger $p_T$ threshold used in the 2018 analysis triggers. The denominator in the efficiency calculation is the number of STA muons with $|\eta| < 1.2$ and $p_T > 28$ GeV.
Level-1 muon trigger efficiency in cosmic-ray muon data (blue) and signal simulation (red) as a function of $d_0$, for the Level-1 trigger $p_T$ threshold used in the 2018 analysis triggers. The denominator in the efficiency calculation is the number of STA muons with $|\eta| < 1.2$ and $p_T > 28$ GeV.
Fractions of signal events with zero (green), one (blue), and two (red) STA muons matched to TMS muons by the STA-to-TMS muon association procedure, as a function of true $L_{xy}$, in all simulated $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal samples combined. The fractions are computed relative to the number of signal events passing the trigger and containing two STA muons with more than 12 muon detector hits and $p_T > 10$ GeV matched to generated muons from $X \rightarrow \mu \mu$ decays.
Fractions of signal events with zero (green), one (blue), and two (red) STA muons matched to TMS muons by the STA-to-TMS muon association procedure, as a function of true $L_{xy}$, in all simulated $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal samples combined. The fractions are computed relative to the number of signal events passing the trigger and containing two STA muons with more than 12 muon detector hits and $p_T > 10$ GeV matched to generated muons from $X \rightarrow \mu \mu$ decays.
Comparison of the number of events observed in 2016 data in the STA-STA dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.
Comparison of the number of events observed in 2016 data in the STA-STA dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.
Comparison of the number of events observed in 2018 data in the STA-STA dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.
Comparison of the number of events observed in 2018 data in the STA-STA dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.
Comparison of the number of events observed in 2016 data in the STA-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 30 and 60 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.
Comparison of the number of events observed in 2016 data in the STA-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 30 and 60 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.
Comparison of the number of events observed in 2018 data in the STA-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 30 and 60 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.
Comparison of the number of events observed in 2018 data in the STA-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 30 and 60 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.
Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $6 < min(d_0 / \sigma_{d_0}) \leq 10$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $6 < min(d_0 / \sigma_{d_0}) \leq 10$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $10 < min(d_0 / \sigma_{d_0}) \leq 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $10 < min(d_0 / \sigma_{d_0}) \leq 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $min(d_0 / \sigma_{d_0}) > 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $min(d_0 / \sigma_{d_0}) > 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $6 < min(d_0 / \sigma_{d_0}) \leq 10$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $6 < min(d_0 / \sigma_{d_0}) \leq 10$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $10 < min(d_0 / \sigma_{d_0}) \leq 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $10 < min(d_0 / \sigma_{d_0}) \leq 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $min(d_0 / \sigma_{d_0}) > 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $min(d_0 / \sigma_{d_0}) > 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, as a function of the smaller of the two $d_0 / \sigma_{d_0}$ values in the TMS-TMS dimuon. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility.
Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, as a function of the smaller of the two $d_0 / \sigma_{d_0}$ values in the TMS-TMS dimuon. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility.
Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, as a function of the smaller of the two $d_0 / \sigma_{d_0}$ values in the TMS-TMS dimuon. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility.
Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, as a function of the smaller of the two $d_0 / \sigma_{d_0}$ values in the TMS-TMS dimuon. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 150\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 150\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 350\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 350\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 10\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 10\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 30\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 30\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 40\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 40\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 60\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 60\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
Observed 95% CL exclusion contours in the HAHM model, in the ($m(Z_D)$, $c\tau(Z_D)$) plane. The contours correspond to several representative values of $B(H \rightarrow Z_DZ_D$) ranging from 0.005 to 1%.
Observed 95% CL exclusion contours in the HAHM model, in the ($m(Z_D)$, $c\tau(Z_D)$) plane. The contours correspond to several representative values of $B(H \rightarrow Z_DZ_D$) ranging from 0.005 to 1%.
Observed 95% CL exclusion contours in the HAHM model, in the ($m(Z_D)$, $\epsilon$) plane. The contours correspond to several representative values of $B(H \rightarrow Z_DZ_D$) ranging from 0.005 to 1%.
Observed 95% CL exclusion contours in the HAHM model, in the ($m(Z_D)$, $\epsilon$) plane. The contours correspond to several representative values of $B(H \rightarrow Z_DZ_D$) ranging from 0.005 to 1%.
Background estimation and observed number of events in the STA-STA dimuon category in 2016 and 2018 data. For each probed LLP mass, the chosen mass interval is shown. The mass interval is followed by the estimated and observed counts for the given year. The quoted uncertainties are statistical only.
Background estimations and observed numbers of events in the STA-STA dimuon category in 2016 and 2018 data. For each probed LLP mass, the chosen mass interval is shown, followed by the predicted background yield $N^\text{est}_\text{bkg}$ and the observed number of events $N^\text{obs}$ for the given year. The quoted uncertainties are statistical only.
Background estimation and observed number of events in the TMS-TMS dimuon category in 2016 data. The mass interval is followed by the estimated and observed counts within each $min(d_0 / \sigma_{d_0})$ bin in this mass interval. The quoted uncertainties are statistical only.
Background estimations and observed numbers of events in the TMS-TMS dimuon category in 2016 data. For each mass interval, the table shows the predicted background yield $N^\text{est}_\text{bkg}$ and the observed number of events $N^\text{obs}$ in each of the three $\text{min}(d_0 / \sigma_{d_0})$ bins. The quoted uncertainties are statistical only
Background estimation and observed number of events in the TMS-TMS dimuon category in 2018 data. The mass interval is followed by the estimated and observed counts within each $min(d_0 / \sigma_{d_0})$ bin in this mass interval. The quoted uncertainties are statistical only.
Background estimations and observed numbers of events in the TMS-TMS dimuon category in 2016 data. For each mass interval, the table shows the predicted background yield $N^\text{est}_\text{bkg}$ and the observed number of events $N^\text{obs}$ in each of the three $\text{min}(d_0 / \sigma_{d_0})$ bins. The quoted uncertainties are statistical only
Correspondence between the mass intervals in the TMS-TMS category and the parameters of the simulated signal samples.
Correspondence between the probed LLP masses and the chosen mass intervals in the TMS-TMS category.
Background estimation and observed number of events in the STA-TMS dimuon category in 2016 and 2018 data. For each probed LLP mass, the chosen mass interval is shown. The mass interval is followed by the estimated and observed counts for the given year. The quoted uncertainties are statistical only.
Background estimations and observed numbers of events in the STA-TMS dimuon category in 2016 and 2018 data. For each probed LLP mass, the chosen mass interval is shown, followed by the predicted background yield $N^\text{est}_\text{bkg}$ and the observed number of events $N^\text{obs}$ for the given year. The quoted uncertainties are statistical only.
Number of events passing consecutive sets of selection criteria for 2018 collision data and the signal process $\Phi(125) \rightarrow XX(20\ GeV, c\tau = 13\ cm) \rightarrow \mu\mu$. Each row introduces a new criterion that is applied in addition to the selection of the previous row. In addition to the total number of events, N(events), the event yields of the individual dimuon vertex categories, STA-STA, TMS-TMS, and STA-TMS, are shown in separate columns for each data set. In these columns, events containing selected dimuons of different categories are independently counted for each category.
Number of events passing consecutive sets of selection criteria, in 2018 data and in a sample of simulated $\Phi \rightarrow XX \rightarrow \mu\mu$ signal events with $m(H) = 125\ GeV$, $m(X) = 20\ GeV$, and $c\tau = 13\ cm$. Each row introduces a new criterion that is applied in addition to the selection of the previous row. In addition to the total number of events $N(\text{total})$, the event yields in the individual dimuon categories, STA-STA, TMS-TMS, and STA-TMS, are shown in separate columns for each data set. In these columns, events containing selected dimuons of different categories are counted independently for each category.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 150\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 150\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 350\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 350\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 150\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 150\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 350\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 350\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 10\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 10\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 30\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 30\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 40\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 40\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 60\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 60\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the TMS-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the TMS-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the TMS-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the TMS-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $70\ cm < L_{xy}^\mathrm{true} < 320\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper. Efficiencies for dimuons with $70\ cm < L_{xy}^\mathrm{true} < 320\ cm$ in the STA-TMS and TMS-TMS dimuon categories are equal to zero.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $70\ cm < L_{xy}^\mathrm{true} < 320\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper. Efficiencies for dimuons with $70\ cm < L_{xy}^\mathrm{true} < 320\ cm$ in the STA-TMS and TMS-TMS dimuon categories are equal to zero.
A measurement of the top quark pole mass $m_\mathrm{t}^\text{pole}$ in events where a top quark-antiquark pair ($\mathrm{t\bar{t}}$) is produced in association with at least one additional jet ($\mathrm{t\bar{t}}$+jet) is presented. This analysis is performed using proton-proton collision data at $\sqrt{s}$ = 13 TeV collected by the CMS experiment at the CERN LHC, corresponding to a total integrated luminosity of 36.3 fb$^{-1}$. Events with two opposite-sign leptons in the final state (e$^+$e$^-$, $\mu^+\mu^-$, e$^\pm\mu^\mp$) are analyzed. The reconstruction of the main observable and the event classification are optimized using multivariate analysis techniques based on machine learning. The production cross section is measured as a function of the inverse of the invariant mass of the $\mathrm{t\bar{t}}$+jet system at the parton level using a maximum likelihood unfolding. Given a reference parton distribution function (PDF), the top quark pole mass is extracted using the theoretical predictions at next-to-leading order. For the ABMP16NLO PDF, this results in $m_\mathrm{t}^\text{pole}$ = 172.93 $\pm$ 1.36 GeV.
Absolute differential cross section as a function of the rho observable at parton level.
Covariance matrix for the total uncertainty (i.e. fit including stat., not extrapolation) for the measurement of the absolute differential cross section as a function of the rho observable at parton level.
Covariance matrix for the statistical uncertainty for the measurement of the absolute differential cross section as a function of the rho observable at parton level.
Covariance matrix for the extrapolation uncertainty for the measurement of the absolute differential cross section as a function of the rho observable at parton level.
Normalized differential cross section as a function of the rho observable at parton level.
Covariance matrix for the total uncertainty (i.e. fit including stat., not extrapolation) for the measurement of the normalized differential cross section as a function of the rho observable at parton level.
Covariance matrix for the statistical uncertainty for the measurement of the normalized differential cross section as a function of the rho observable at parton level.
Covariance matrix for the extrapolation uncertainty for the measurement of the normalized differential cross section as a function of the rho observable at parton level.
Correlation matrix for all nuisance parameters and parameters of interest of the Likelihood fit.
This table is a numerical representation of Fig. 8 for all nuisance parameters.
A search is presented for long-lived particles produced in pairs in proton-proton collisions at the LHC operating at a center-of-mass energy of 13 TeV. The data were collected with the CMS detector during the period from 2015 through 2018, and correspond to a total integrated luminosity of 140 fb$^{-1}$. This search targets pairs of long-lived particles with mean proper decay lengths between 0.1 and 100 mm, each of which decays into at least two quarks that hadronize to jets, resulting in a final state with two displaced vertices. No significant excess of events with two displaced vertices is observed. In the context of $R$-parity violating supersymmetry models, the pair production of long-lived neutralinos, gluinos, and top squarks is excluded at 95% confidence level for cross sections larger than 0.08 fb, masses between 800 and 3000 GeV, and mean proper decay lengths between 1 and 25 mm.
Event yields in the control samples in data. The ''one-vertex'' events correspond to events containing exactly one vertex with the specified number of tracks. The ''two-vertex'' events have two or more vertices containing the specified numbers of tracks. We seek the signal in the $\geq$5-track two-vertex sample.
The distribution of distances between vertices in the $x$-$y$ plane, $d_{\mathrm{VV}}$, for three simulated multijet signals each with a mass of 1600 GeV, with the background template distribution overlaid. The production cross section for each signal model is assumed to be the lower limit excluded by CMS-EXO-17-018, corresponding to values of 0.8, 0.25, and 0.15 fb for the samples with $c\tau =$ 0.3, 1.0, and 10 mm, respectively. The last bin includes the overflow events. The two vertical pink dashed lines separate the regions used in the fit.
Multijet signal efficiencies as a function of the signal mass and lifetime for events satisfying all event and vertex requirements, with corrections based on systematic differences in the vertex reconstruction efficiency between data and simulation.
Dijet signal efficiencies as a function of the signal mass and lifetime for events satisfying all event and vertex requirements, with corrections based on systematic differences in the vertex reconstruction efficiency between data and simulation.
The distribution of $d_{\mathrm{BV}}$ for $\geq$5-track one-vertex events in data and three simulated multijet signal samples each with a mass of 1600 GeV. The production cross section for each signal model is assumed to be the lower limit excluded by CMS-EXO-17-018, corresponding to values of 0.8, 0.25, and 0.15 fb for the samples with $c\tau =$ 0.3, 1.0, and 10 mm, respectively. The last bin includes the overflow events. This bin includes one event in data with a vertex with large $d_{\mathrm{BV}}$ that appears to arise from tracks originating from separate pp interaction vertices, consistent with background.
Distribution of the $x$-$y$ distances between vertices, $d_{\mathrm{VV}}$, for 2017 and 2018 data. The background distribution $d_{\mathrm{VV}}^{\kern 0.15em\mathrm{C}}$ (blue continuous line) is constructed from one-vertex events in data, and is normalized to the number of two-vertex events in data with two 3-track vertices. The two vertical red dashed lines separate the regions used in the fit.
Distribution of the $x$-$y$ distances between vertices, $d_{\mathrm{VV}}$, for 2017 and 2018 data. The background distribution $d_{\mathrm{VV}}^{\kern 0.15em\mathrm{C}}$ (blue continuous line) is constructed from one-vertex events in data, and is normalized to the number of two-vertex events in data which have exactly one 4-track vertex and one 3-track vertex. The two vertical red dashed lines separate the regions used in the fit.
Distribution of the $x$-$y$ distances between vertices, $d_{\mathrm{VV}}$, for 2017 and 2018 data. The background distribution $d_{\mathrm{VV}}^{\kern 0.15em\mathrm{C}}$ (blue continuous line) is constructed from one-vertex events in data, and is normalized to the number of two-vertex events in data with two 4-track vertices. The two vertical red dashed lines separate the regions used in the fit.
Distribution of the $x$-$y$ distances between vertices, $d_{\mathrm{VV}}$, for 2017 and 2018 data. The background distribution $d_{\mathrm{VV}}^{\kern 0.15em\mathrm{C}}$ (blue continuous line) is constructed from one-vertex events in data, and is normalized using $\geq$5-track one-vertex event information. The two vertical red dashed lines separate the regions used in the fit.
Predicted yields for the background-only normalized template, predicted yields for three simulated multijet signals each with a mass of 1600 GeV, and the observed yield in each $d_{\mathrm{VV}}$ bin. The production cross section for each signal model is assumed to be the lower limit excluded by CMS-EXO-17-018, corresponding to values of 0.8, 0.25, and 0.15 fb for samples with $c\tau =$ 0.3, 1.0, and 10 mm, respectively. The uncertainties in the signal yields and the systematic uncertainties in the background prediction reflect the systematic uncertainties given in the text.
Observed 95% CL upper limits on the product of cross section and branching fraction squared for the multijet signals, as a function of mass and $c\tau$. The overlaid mass-lifetime exclusion curves assume pair-production cross sections for the neutralino (red) and gluino (purple) with 100% branching fraction to each model's respective decay mode specified. The solid black (dashed colored) lines represent the observed (median expected) limits at 95% CL. The thin black lines represent the variation of the observed limit within theoretical uncertainties of the signal cross section. The thin dashed colored lines represent the region containing 68% of the expected limit distribution under the background-only hypothesis. The observed limits from the CMS displaced jets search (CMS-EXO-19-021) are also shown in teal for comparison.
Observed 95% CL upper limits on the product of cross section and branching fraction squared for the multijet signals, as a function of mass and $c\tau$. The overlaid mass-lifetime exclusion curves assume pair-production cross sections for the neutralino (red) and gluino (purple) with 100% branching fraction to each model's respective decay mode specified. The solid black (dashed colored) lines represent the observed (median expected) limits at 95% CL. The thin black lines represent the variation of the observed limit within theoretical uncertainties of the signal cross section. The thin dashed colored lines represent the region containing 68% of the expected limit distribution under the background-only hypothesis. The observed limits from the CMS displaced jets search (CMS-EXO-19-021) are also shown in teal for comparison.
Observed 95% CL upper limits on the product of cross section and branching fraction squared for the multijet signals, as a function of mass and $c\tau$. The overlaid mass-lifetime exclusion curves assume pair-production cross sections for the neutralino (red) and gluino (purple) with 100% branching fraction to each model's respective decay mode specified. The solid black (dashed colored) lines represent the observed (median expected) limits at 95% CL. The thin black lines represent the variation of the observed limit within theoretical uncertainties of the signal cross section. The thin dashed colored lines represent the region containing 68% of the expected limit distribution under the background-only hypothesis. The observed limits from the CMS displaced jets search (CMS-EXO-19-021) are also shown in teal for comparison.
Observed 95% CL upper limits on the product of cross section and branching fraction squared for the dijet signals, as a function of mass and $c\tau$. The overlaid mass-lifetime exclusion curves assume pair-production cross sections for the top squark with 100% branching fraction to each model's respective decay mode specified. The solid black (dashed colored) lines represent the observed (median expected) limits at 95% CL. The thin black lines represent the variation of the observed limit within theoretical uncertainties of the signal cross section. The thin dashed colored lines represent the region containing 68% of the expected limit distribution under the background-only hypothesis. The observed limits from the CMS displaced jets search (CMS-EXO-19-021) are also shown in teal for comparison.
Observed 95% CL upper limits on the product of cross section and branching fraction squared for the dijet signals, as a function of mass and $c\tau$. The overlaid mass-lifetime exclusion curves assume pair-production cross sections for the top squark with 100% branching fraction to each model's respective decay mode specified. The solid black (dashed colored) lines represent the observed (median expected) limits at 95% CL. The thin black lines represent the variation of the observed limit within theoretical uncertainties of the signal cross section. The thin dashed colored lines represent the region containing 68% of the expected limit distribution under the background-only hypothesis. The observed limits from the CMS displaced jets search (CMS-EXO-19-021) are also shown in teal for comparison.
Observed and expected 95% CL upper limits on the product of cross section and branching fraction squared, as a function of mass for multijet signals, for a fixed $c\tau$ of 300um in the full Run-2 data set. The neutralino and gluino pair production cross sections are shown for the multijet signals.
Observed and expected 95% CL upper limits on the product of cross section and branching fraction squared, as a function of mass for dijet signals, for a fixed $c\tau$ of 300um in the full Run-2 data set. The top squark pair-production cross section is shown for the dijet signals.
Observed and expected 95% CL upper limits on the product of cross section and branching fraction squared, as a function of mass for multijet signals, for a fixed $c\tau$ of 1 mm in the full Run-2 data set. The neutralino and gluino pair production cross sections are shown for the multijet signals.
Observed and expected 95% CL upper limits on the product of cross section and branching fraction squared, as a function of mass for dijet signals, for a fixed $c\tau$ of 1 mm in the full Run-2 data set. The top squark pair-production cross section is shown for the dijet signals.
Observed and expected 95% CL upper limits on the product of cross section and branching fraction squared, as a function of mass for multijet signals, for a fixed $c\tau$ of 10 mm in the full Run-2 data set. The neutralino and gluino pair production cross sections are shown for the multijet signals.
Observed and expected 95% CL upper limits on the product of cross section and branching fraction squared, as a function of mass for dijet signals, for a fixed $c\tau$ of 10 mm in the full Run-2 data set. The top squark pair-production cross section is shown for the dijet signals.
Observed and expected 95% CL upper limits on the product of cross section and branching fraction squared, as a function of $c\tau$ for multijet signals, for a fixed mass of 800 GeV in the full Run-2 data set. The neutralino and gluino pair production cross sections are shown for the multijet signals.
Observed and expected 95% CL upper limits on the product of cross section and branching fraction squared, as a function of $c\tau$ for dijet signals, for a fixed mass of 800 GeV in the full Run-2 data set. The top squark pair-production cross section is shown for the dijet signals.
Observed and expected 95% CL upper limits on the product of cross section and branching fraction squared, as a function of $c\tau$ for multijet signals, for a fixed mass of 1600 GeV in the full Run-2 data set. The neutralino and gluino pair production cross sections are shown for the multijet signals.
Observed and expected 95% CL upper limits on the product of cross section and branching fraction squared, as a function of $c\tau$ for dijet signals, for a fixed mass of 1600 GeV in the full Run-2 data set. The top squark pair-production cross section is shown for the dijet signals.
Observed and expected 95% CL upper limits on the product of cross section and branching fraction squared, as a function of $c\tau$ for multijet signals, for a fixed mass of 2400 GeV in the full Run-2 data set. The neutralino and gluino pair production cross sections are shown for the multijet signals. For $m$ = 2400 GeV, the expected neutralino cross section is $\approx 8\times 10^{-5}$ fb and is not shown.
Observed and expected 95% CL upper limits on the product of cross section and branching fraction squared, as a function of $c\tau$ for dijet signals, for a fixed mass of 2400 GeV in the full Run-2 data set. The top squark pair-production cross section is shown for the dijet signals.
Data-to-simulation efficiency correction factors, shown for multijet and dijet signal topologies in several ranges of $c\tau$. Note that these correction factors account for the two long-lived particles in the simulated events, and are therefore the total correction factors used to scale event yields rather than the correction factors one would apply to individual vertices.
Distribution of the azimuthal angle between vertices, $\Delta\phi_{\mathrm{VV}}$, for 2017 and 2018 data. The background distribution (blue continuous line) is constructed from 3-track one-vertex events in data, and is normalized to the number of 3-track two-vertex events in data.
Distribution of the azimuthal angle between vertices, $\Delta\phi_{\mathrm{VV}}$, for 2017 and 2018 data. The background distribution (blue continuous line) is constructed from 4-track and 3-track one-vertex events in data, and is normalized to the number of two-vertex events in data which have exactly one 4-track vertex and one 3-track vertex.
Distribution of the azimuthal angle between vertices, $\Delta\phi_{\mathrm{VV}}$, for 2017 and 2018 data. The background distribution (blue continuous line) is constructed from 4-track one-vertex events in data, and is normalized to the number of 4-track two-vertex events in data.
Distribution of the azimuthal angle between vertices, $\Delta\phi_{\mathrm{VV}}$, for 2017 and 2018 data. The background distribution (blue continuous line) is constructed from $\geq$5-track one-vertex events in data, and is normalized using one-vertex event information. No $\geq$5-track two-vertex data events pass the selection.
A search for a heavy resonance decaying into a top quark and a W boson in proton-proton collisions at $\sqrt{s} =$ 13 TeV is presented. The data analyzed were recorded with the CMS detector at the LHC and correspond to an integrated luminosity of 138 fb$^{-1}$. The top quark is reconstructed as a single jet and the W boson, from its decay into an electron or muon and the corresponding neutrino. A top quark tagging technique based on jet clustering with a variable distance parameter and simultaneous jet grooming is used to identify jets from the collimated top quark decay. The results are interpreted in the context of two benchmark models, where the heavy resonance is either an excited bottom quark b$^*$ or a vector-like quark B. A statistical combination with an earlier search by the CMS Collaboration in the all-hadronic final state is performed to place upper cross section limits on these two models. The new analysis extends the lower range of resonance mass probed from 1.4 down to 0.7 TeV. For left-handed, right-handed, and vector-like couplings, b$^*$ masses up to 3.0, 3.0, and 3.2 TeV are excluded at 95% confidence level, respectively. The observed upper limits represent the most stringent constraints on the b$^*$ model to date.
Distributions of MtW in the 1b category. The data are shown by filled markers, where the horizontal bars indicate the bin widths. The individual background contributions are given by filled histograms. The expected signal for a LH b* with mb∗ = 2.4 TeV is shown by a dashed line. The shaded region is the uncertainty in the total background estimate. The lower panel shows the ratio of data to the background estimate, with the total uncertainty on the predicted background displayed as the gray band.
Distributions of MtW in the 2b category. The data are shown by filled markers, where the horizontal bars indicate the bin widths. The individual background contributions are given by filled histograms. The expected signal for a LH b* with mb∗ = 2.4 TeV is shown by a dashed line. The shaded region is the uncertainty in the total background estimate. The lower panel shows the ratio of data to the background estimate, with the total uncertainty on the predicted background displayed as the gray band.
Upper limits on the production cross section times branching fraction of the b* LH hypothesis at a 95% CL. Dashed colored lines show the expected limits from the l+jets and all-hadronic channels, where the latter start at resonance masses of 1.4 TeV. The observed and expected limits from the combination are shown as solid and dashed black lines, respectively. The green and yellow bands show the 68 and 95% confidence intervals on the combined expected limits.
Upper limits on the production cross section times branching fraction of the b* RH hypothesis at a 95% CL. Dashed colored lines show the expected limits from the l+jets and all-hadronic channels, where the latter start at resonance masses of 1.4 TeV. The observed and expected limits from the combination are shown as solid and dashed black lines, respectively. The green and yellow bands show the 68 and 95% confidence intervals on the combined expected limits.
Upper limits on the production cross section times branching fraction of the b* VL hypothesis at a 95% CL. Dashed colored lines show the expected limits from the l+jets and all-hadronic channels, where the latter start at resonance masses of 1.4 TeV. The observed and expected limits from the combination are shown as solid and dashed black lines, respectively. The green and yellow bands show the 68 and 95% confidence intervals on the combined expected limits.
Upper limits on the production cross section times branching fraction of the B+b hypothesis at a 95% CL. Dashed colored lines show the expected limits from the l+jets and all-hadronic channels, where the latter start at resonance masses of 1.4 TeV. The observed and expected limits from the combination are shown as solid and dashed black lines, respectively. The green and yellow bands show the 68 and 95% confidence intervals on the combined expected limits.
Upper limits on the production cross section times branching fraction of the B+t hypothesis at a 95% CL. Dashed colored lines show the expected limits from the l+jets and all-hadronic channels, where the latter start at resonance masses of 1.4 TeV. The observed and expected limits from the combination are shown as solid and dashed black lines, respectively. The green and yellow bands show the 68 and 95% confidence intervals on the combined expected limits.
A search is reported for heavy resonances and quantum black holes decaying into e$\mu$, e$\tau$, and $\mu\tau$ final states in proton-proton collision data recorded by the CMS experiment at the CERN LHC during 2016-2018 at $\sqrt{s}$ = 13 TeV, corresponding to an integrated luminosity of 138 fb$^{-1}$. The e$\mu$, e$\tau$, and $\mu\tau$ invariant mass spectra are reconstructed, and no evidence is found for physics beyond the standard model. Upper limits are set at 95% confidence level on the product of the cross section and branching fraction for lepton flavor violating signals. Three benchmark signals are studied: resonant $\tau$ sneutrino production in $R$ parity violating supersymmetric models, heavy Z' gauge bosons with lepton flavor violating decays, and nonresonant quantum black hole production in models with extra spatial dimensions. Resonant $\tau$ sneutrinos are excluded for masses up to 4.2 TeV in the e$\mu$ channel, 3.7 TeV in the e$\tau$ channel, and 3.6 TeV in the $\mu\tau$ channel. A Z' boson with lepton flavor violating couplings is excluded up to a mass of 5.0 TeV in the e$\mu$ channel, up to 4.3 TeV in the e$\tau$ channel, and up to 4.1 TeV in the $\mu\tau$ channel. Quantum black holes in the benchmark model are excluded up to the threshold mass of 5.6 TeV in the e$\mu$ channel, 5.2 TeV in the e$\tau$ channel, and 5.0 TeV in the $\mu\tau$ channel. In addition, model-independent limits are extracted to allow comparisons with other models for the same final states and similar event selection requirements. The results of these searches provide the most stringent limits available from collider experiments for heavy particles that undergo lepton flavor violating decays.
Mass distributions for the e$\mu$ channel. In addition to the observed data (black points) and SM prediction (filled histograms), expected signal distributions for three models are shown: the RPV SUSY model with $\lambda = \lambda' = 0.01$ and $\tau$ sneutrino mass of 1.6 TeV, a Z′ boson ($\mathcal{B}=0.1$) with a mass of 1.6 TeV , and the QBH signal expectation for $n=4$ and a threshold mass of 1.6 TeV. The bin width gradually increases with mass.
Mass distributions for the e$\tau$ channel. In addition to the observed data (black points) and SM prediction (filled histograms), expected signal distributions for three models are shown: the RPV SUSY model with $\lambda = \lambda' = 0.01$ and $\tau$ sneutrino mass of 1.6 TeV, a Z′ boson ($\mathcal{B}=0.1$) with a mass of 1.6 TeV , and the QBH signal expectation for $n=4$ and a threshold mass of 1.6 TeV. The bin width gradually increases with mass.
Mass distributions for the $\mu\tau$ channel. In addition to the observed data (black points) and SM prediction (filled histograms), expected signal distributions for three models are shown: the RPV SUSY model with $\lambda = \lambda' = 0.01$ and $\tau$ sneutrino mass of 1.6 TeV, a Z′ boson ($\mathcal{B}=0.1$) with a mass of 1.6 TeV , and the QBH signal expectation for $n=4$ and a threshold mass of 1.6 TeV. The bin width gradually increases with mass.
The observed and expected (in parentheses) 95% CL lower mass limits on RPV SUSY, Z′ ($\mathcal{B}=0.1$) , and QBH signals for the e$\mu$, e$\tau$, and $\mu\tau$ channels.
Expected and observed 95% CL upper limits on the product of cross section times branching fraction as a function of the $ au$ sneutrino mass in an RPV SUSY model for the e$\mu$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red and blue solid lines show the product of cross section times branching fraction as a function of the tau sneutrino mass for two different values of couplings.
Expected and observed 95% CL upper limits on the product of cross section times branching fraction as a function of the $ au$ sneutrino mass in an RPV SUSY model for the e$\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red and blue solid lines show the product of cross section times branching fraction as a function of the tau sneutrino mass for two different values of couplings.
Expected and observed 95% CL upper limits on the product of cross section times branching fraction as a function of the $ au$ sneutrino mass in an RPV SUSY model for the $\mu\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red and blue solid lines show the product of cross section times branching fraction as a function of the tau sneutrino mass for two different values of couplings.
Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for a Z′ ($\mathcal{B}=0.1$) boson with LFV decays, in the e$\mu$ channel.The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the Z′ mass.
Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for a Z′ ($\mathcal{B}=0.1$) boson with LFV decays, in the e$\tau$ channel.The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the Z′ mass.
Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for a Z′ ($\mathcal{B}=0.1$) boson with LFV decays, in the $\mu\tau$ channel.The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the Z′ mass.
Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for quantum black hole production in an ADD model with $n=4$ extra dimensions, in the e$\mu$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the QBH threshold mass.
Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for quantum black hole production in an ADD model with $n=4$ extra dimensions, in the e$\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the QBH threshold mass.
Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for quantum black hole production in an ADD model with $n=4$ extra dimensions, in the $\mu\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the QBH threshold mass.
Upper limits at 95% CL on the RPV SUSY model in the plane of $\tau$ sneutrino mass and $\lambda'$ coupling, for four values of $\lambda$ couplings for the e$\mu$ channel. The regions to the left of and above the curves are excluded.
Upper limits at 95% CL on the RPV SUSY model in the plane of $\tau$ sneutrino mass and $\lambda'$ coupling, for four values of $\lambda$ couplings for the e$\tau$ channel. The regions to the left of and above the curves are excluded.
Upper limits at 95% CL on the RPV SUSY model in the plane of $\tau$ sneutrino mass and $\lambda'$ coupling, for four values of $\lambda$ couplings for the $\mu\tau$ channel. The regions to the left of and above the curves are excluded.
Model-independent upper limits at 95% CL on the product of cross section, branching fraction, and acceptance are shown. Observed (expected) limits are shown in black solid (dashed) lines for the e$\mu$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits.
Model-independent upper limits at 95% CL on the product of cross section, branching fraction, and acceptance are shown. Observed (expected) limits are shown in black solid (dashed) lines for the e$\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits.
Model-independent upper limits at 95% CL on the product of cross section, branching fraction, and acceptance are shown. Observed (expected) limits are shown in black solid (dashed) lines for the $\mu\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits.
Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning from Figure 2.
Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning from Figure 2.
Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning from Figure 2.
Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning used in Figure 2.
Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning used in Figure 2.
Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning used in Figure 2.
The structure of nucleons is multidimensional and depends on the transverse momenta, spatial geometry, and polarization of the constituent partons. Such a structure can be studied using high-energy photons produced in ultraperipheral heavy-ion collisions. The first measurement of the azimuthal angular correlations of exclusively produced events with two jets in photon-lead interactions at large momentum transfer is presented, a process that is considered to be sensitive to the underlying nuclear gluon polarization. This study uses a data sample of ultraperipheral lead-lead collisions at $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV, corresponding to an integrated luminosity of 0.38 nb$^{-1}$, collected with the CMS experiment at the LHC. The measured second harmonic of the correlation between the sum and difference of the two jet momenta is found to be positive, and rising, as the dijet momentum increases. A well-tuned model that has been successful at describing a wide range of proton scattering data from the HERA experiments fails to describe the observed correlations, suggesting the presence of gluon polarization effects.
The unfolded 1/${N_{\mathrm{events}}} dN/d\Phi$ distribution
The unfolded $<\cos(2\Phi)>$ distribution as a function of $Q_{\mathrm{T}}$
When you search on a word, e.g. 'collisions', we will automatically search across everything we store about a record. But, sometimes you may wish to be more specific. Here we show you how.
Guidance and examples on the query string syntax can be found in the Elasticsearch documentation.
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