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A search is presented for single production of a vector-like T quark with charge 2/3 $e$, in the decay channel featuring a top quark and a Z boson, with the top quark decaying hadronically and the Z boson decaying to neutrinos. The search uses data collected by the CMS experiment in proton-proton collisions at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 137 fb$^{-1}$ recorded at the CERN LHC in 2016-2018. The search is sensitive to a T quark mass between 0.6 and 1.8 TeV with decay widths ranging from negligibly small up to 30% of the T quark mass. Reconstruction strategies for the top quark are based on the degree of Lorentz boosting of its final state. At 95% confidence level, the upper limit on the product of the cross section and branching fraction for a T quark of small decay width varies between 15 and 602 fb, depending on its mass. For a T quark with decay widths between 10 and 30% of its mass, this upper limit ranges between 16 and 836 fb. For most of the studied range, the results provide the best limits to date. This is the first search for single T quark production based on the full Run 2 data set of the LHC.
Product of efficiency and acceptance of the event selection for T signal events as a function of the particle mass $m_\mathrm{T}$ and width $\Gamma$ for the different hypotheses considered.
Product of efficiency and acceptance of the event selection for T signal events as a function of the particle mass $m_\mathrm{T}$ and width $\Gamma$ for the different hypotheses considered.
Product of efficiency and acceptance of the event selection for T signal events as a function of the particle mass $m_\mathrm{T}$ and width $\Gamma$ for the different hypotheses considered.
Product of efficiency and acceptance of the event selection for T signal events as a function of the particle mass $m_\mathrm{T}$ and width $\Gamma$ for the different hypotheses considered.
Product of efficiency and acceptance of the event selection for T signal events as a function of the particle mass $m_\mathrm{T}$ and width $\Gamma$ for the different hypotheses considered.
Product of efficiency and acceptance of the event selection for T signal events as a function of the particle mass $m_\mathrm{T}$ and width $\Gamma$ for the different hypotheses considered.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged validation region with 0 forward jets. Data are compared to backgrounds are after the data-driven extraction is performed.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged validation region with at least 1 forward jet. Data are compared to backgrounds are after the data-driven extraction is performed.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged validation region with 0 forward jets. Data are compared to backgrounds are after the data-driven extraction is performed.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged validation region with at least 1 forward jet. Data are compared to backgrounds are after the data-driven extraction is performed.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged validation region with 0 forward jets. Data are compared to backgrounds are after the data-driven extraction is performed.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged validation region with at least 1 forward jet. Data are compared to backgrounds are after the data-driven extraction is performed.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved validation region with 0 forward jets. Data are compared to backgrounds are after the data-driven extraction is performed.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved validation region with at least 1 forward jet. Data are compared to backgrounds are after the data-driven extraction is performed.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved validation region with 0 forward jets. Data are compared to backgrounds are after the data-driven extraction is performed.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved validation region with at least 1 forward jet. Data are compared to backgrounds are after the data-driven extraction is performed.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved validation region with 0 forward jets. Data are compared to backgrounds are after the data-driven extraction is performed.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved validation region with at least 1 forward jet. Data are compared to backgrounds are after the data-driven extraction is performed.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged region with 0 forward jets. Data are compared to backgrounds obtained post-fit.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged region with at least 1 forward jet. Data are compared to backgrounds obtained post-fit.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged region with 0 forward jets. Data are compared to backgrounds obtained post-fit.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged region with at least 1 forward jet. Data are compared to backgrounds obtained post-fit.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged region with 0 forward jets. Data are compared to backgrounds obtained post-fit.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged region with at least 1 forward jet. Data are compared to backgrounds obtained post-fit.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the partially merged region with 0 forward jets. Data are compared to backgrounds obtained post-fit.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the partially merged region with at least 1 forward jet. Data are compared to backgrounds obtained post-fit.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the partially merged region with 0 forward jets. Data are compared to backgrounds obtained post-fit.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the partially merged region with at least 1 forward jet. Data are compared to backgrounds obtained post-fit.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the partially merged region with 0 forward jets. Data are compared to backgrounds obtained post-fit.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the partially merged region with at least 1 forward jet. Data are compared to backgrounds obtained post-fit.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved region with 0 forward jets. Data are compared to backgrounds obtained post-fit.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved region with at least 1 forward jet. Data are compared to backgrounds obtained post-fit.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved region with 0 forward jets. Data are compared to backgrounds obtained post-fit.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved region with at least 1 forward jet. Data are compared to backgrounds obtained post-fit.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved region with 0 forward jets. Data are compared to backgrounds obtained post-fit.
Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved region with at least 1 forward jet. Data are compared to backgrounds obtained post-fit.
Observed and expected 95% CL upper limits on the product of the cross section and branching fraction of a T decaying to a hadronic top quark and a Z boson to neutrinos as a function of the T mass, for a T with a width of 0.01 $ imes$ its mass.
Observed and expected 95% CL upper limits on the product of the cross section and branching fraction of a T decaying to a hadronic top quark and a Z boson to neutrinos as a function of the T mass, for a T with a width of 0.1 $ imes$ its mass.
Observed and expected 95% CL upper limits on the product of the cross section and branching fraction of a T decaying to a hadronic top quark and a Z boson to neutrinos as a function of the T mass, for a T with a width of 0.2 $ imes$ its mass.
Observed and expected 95% CL upper limits on the product of the cross section and branching fraction of a T decaying to a hadronic top quark and a Z boson to neutrinos as a function of the T mass, for a T with a width of 0.3 $ imes$ its mass.
Model independent observed 95% CL upper limits on the product of the cross section and branching fraction of a T decaying to a top quark and a Z boson as a function of the T mass and width.
Singlet model 95% CL excluded values of the product of the cross section and branching fraction for a T decaying to a top quark and a Z boson.
The first collider search for dark matter arising from a strongly coupled hidden sector is presented and uses a data sample corresponding to 138 fb$^{-1}$, collected with the CMS detector at the CERN LHC, at $\sqrt{s} =$ 13 TeV. The hidden sector is hypothesized to couple to the standard model (SM) via a heavy leptophobic Z' mediator produced as a resonance in proton-proton collisions. The mediator decay results in two "semivisible" jets, containing both visible matter and invisible dark matter. The final state therefore includes moderate missing energy aligned with one of the jets, a signature ignored by most dark matter searches. No structure in the dijet transverse mass spectra compatible with the signal is observed. Assuming the Z' has a universal coupling of 0.25 to the SM quarks, an inclusive search, relevant to any model that exhibits this kinematic behavior, excludes mediator masses of 1.5-4.0 TeV at 95% confidence level, depending on the other signal model parameters. To enhance the sensitivity of the search for this particular class of hidden sector models, a boosted decision tree (BDT) is trained using jet substructure variables to distinguish between semivisible jets and SM jets from background processes. When the BDT is employed to identify each jet in the dijet system as semivisible, the mediator mass exclusion increases to 5.1 TeV, for wider ranges of the other signal model parameters. These limits exclude a wide range of strongly coupled hidden sector models for the first time.
The normalized distribution of the characteristic variable $R_{\text{T}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the characteristic variable $R_{\text{T}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the characteristic variable $R_{\text{T}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the characteristic variable $\Delta\phi_{\text{min}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the characteristic variable $\Delta\phi_{\text{min}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the characteristic variable $\Delta\phi_{\text{min}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $m_{\text{SD}}$ for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $m_{\text{SD}}$ for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $m_{\text{SD}}$ for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $D_{p_{\text{T}}}$ for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $D_{p_{\text{T}}}$ for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $D_{p_{\text{T}}}$ for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized BDT discriminator distribution for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models.
The normalized BDT discriminator distribution for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models.
The normalized BDT discriminator distribution for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The $m_{\text{T}}$ distribution for the high-$R_{\text{T}}$ signal region, comparing the observed data to the background prediction from the analytic fit ($g_{3}(x) = \exp(p_{1}x)x^{p_{2}(1+p_{3}\ln(x))}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The $m_{\text{T}}$ distribution for the high-$R_{\text{T}}$ signal region, comparing the observed data to the background prediction from the analytic fit ($g_{3}(x) = \exp(p_{1}x)x^{p_{2}(1+p_{3}\ln(x))}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The $m_{\text{T}}$ distribution for the high-$R_{\text{T}}$ signal region, comparing the observed data to the background prediction from the analytic fit ($g_{3}(x) = \exp(p_{1}x)x^{p_{2}(1+p_{3}\ln(x))}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The $m_{\text{T}}$ distribution for the low-$R_{\text{T}}$ signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The $m_{\text{T}}$ distribution for the low-$R_{\text{T}}$ signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The $m_{\text{T}}$ distribution for the low-$R_{\text{T}}$ signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The $m_{\text{T}}$ distribution for the high-SVJ2 signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The $m_{\text{T}}$ distribution for the high-SVJ2 signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The $m_{\text{T}}$ distribution for the high-SVJ2 signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The $m_{\text{T}}$ distribution for the low-SVJ2 signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The $m_{\text{T}}$ distribution for the low-SVJ2 signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The $m_{\text{T}}$ distribution for the low-SVJ2 signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the dark hadron mass.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the dark hadron mass.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the dark hadron mass.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the invisible fraction.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the invisible fraction.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the invisible fraction.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the dark hadron mass.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the dark hadron mass.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the dark hadron mass.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the invisible fraction.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the invisible fraction.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the invisible fraction.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The 95% CL upper limits on the product of the cross section and branching fraction from the inclusive search for the $\alpha_{\text{dark}}$ variations.
The 95% CL upper limits on the product of the cross section and branching fraction from the inclusive search for the $\alpha_{\text{dark}}$ variations.
The 95% CL upper limits on the product of the cross section and branching fraction from the inclusive search for the $\alpha_{\text{dark}}$ variations.
The 95% CL upper limits on the product of the cross section and branching fraction from the BDT-based search for the $\alpha_{\text{dark}}$ variations.
The 95% CL upper limits on the product of the cross section and branching fraction from the BDT-based search for the $\alpha_{\text{dark}}$ variations.
The 95% CL upper limits on the product of the cross section and branching fraction from the BDT-based search for the $\alpha_{\text{dark}}$ variations.
The three two-dimensional signal model parameter scans.
The three two-dimensional signal model parameter scans.
The three two-dimensional signal model parameter scans.
Metrics representing the performance of the BDT for the benchmark signal model ($m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$), compared to each of the major SM background processes.
Metrics representing the performance of the BDT for the benchmark signal model ($m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$), compared to each of the major SM background processes.
Metrics representing the performance of the BDT for the benchmark signal model ($m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$), compared to each of the major SM background processes.
The range of effects on the signal yield for each systematic uncertainty and the total. Values less than 0.01% are rounded to 0.0%.
The range of effects on the signal yield for each systematic uncertainty and the total. Values less than 0.01% are rounded to 0.0%.
The range of effects on the signal yield for each systematic uncertainty and the total. Values less than 0.01% are rounded to 0.0%.
The normalized distribution of the variable $m_{\text{T}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $m_{\text{T}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $m_{\text{T}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $\Delta\eta(\text{J}_{1},\text{J}_{2})$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $\Delta\eta(\text{J}_{1},\text{J}_{2})$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $\Delta\eta(\text{J}_{1},\text{J}_{2})$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $p_{\text{T}}^{\text{miss}}$ for the simulated SM backgrounds and several signal models. The $R_{\text{T}}$ requirement is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $p_{\text{T}}^{\text{miss}}$ for the simulated SM backgrounds and several signal models. The $R_{\text{T}}$ requirement is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $p_{\text{T}}^{\text{miss}}$ for the simulated SM backgrounds and several signal models. The $R_{\text{T}}$ requirement is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $N_{\text{e}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $N_{\text{e}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $N_{\text{e}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $N_{\mu}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $N_{\mu}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $N_{\mu}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of $\Delta\eta(\text{J}_{1},\text{J}_{2})$ vs. $R_{\text{T}}$ for the simulated QCD background. The preselection requirements on both variables are omitted, but all other preselection requirements are applied.
The normalized distribution of $\Delta\eta(\text{J}_{1},\text{J}_{2})$ vs. $R_{\text{T}}$ for the simulated QCD background. The preselection requirements on both variables are omitted, but all other preselection requirements are applied.
The normalized distribution of $\Delta\eta(\text{J}_{1},\text{J}_{2})$ vs. $R_{\text{T}}$ for the simulated QCD background. The preselection requirements on both variables are omitted, but all other preselection requirements are applied.
The normalized distribution of $p_{\text{T}}^{\text{miss}}$ vs. $m_{\text{T}}$ for the simulated QCD background. All selection requirements are omitted, except for the requirement of two high-$p_{\text{T}}$ wide jets.
The normalized distribution of $p_{\text{T}}^{\text{miss}}$ vs. $m_{\text{T}}$ for the simulated QCD background. All selection requirements are omitted, except for the requirement of two high-$p_{\text{T}}$ wide jets.
The normalized distribution of $p_{\text{T}}^{\text{miss}}$ vs. $m_{\text{T}}$ for the simulated QCD background. All selection requirements are omitted, except for the requirement of two high-$p_{\text{T}}$ wide jets.
The normalized distribution of $R_{\text{T}}$ vs. $m_{\text{T}}$ for the simulated QCD background. All selection requirements are omitted, except for the requirement of two high-$p_{\text{T}}$ wide jets.
The normalized distribution of $R_{\text{T}}$ vs. $m_{\text{T}}$ for the simulated QCD background. All selection requirements are omitted, except for the requirement of two high-$p_{\text{T}}$ wide jets.
The normalized distribution of $R_{\text{T}}$ vs. $m_{\text{T}}$ for the simulated QCD background. All selection requirements are omitted, except for the requirement of two high-$p_{\text{T}}$ wide jets.
The normalized distributions of the BDT input variable $\tau_{21}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\tau_{21}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\tau_{21}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\tau_{32}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\tau_{32}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\tau_{32}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $N_{2}^{(1)}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $N_{2}^{(1)}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $N_{2}^{(1)}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $N_{3}^{(1)}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $N_{3}^{(1)}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $N_{3}^{(1)}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $g_{\text{jet}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $g_{\text{jet}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $g_{\text{jet}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\sigma_{\text{major}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\sigma_{\text{major}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\sigma_{\text{major}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\sigma_{\text{minor}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\sigma_{\text{minor}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\sigma_{\text{minor}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\Delta\phi(\vec{J},\vec{p}_{\text{T}}^{\text{miss}})$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\Delta\phi(\vec{J},\vec{p}_{\text{T}}^{\text{miss}})$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\Delta\phi(\vec{J},\vec{p}_{\text{T}}^{\text{miss}})$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\text{h}^{\pm}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\text{h}^{\pm}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\text{h}^{\pm}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\text{e}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\text{e}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\text{e}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\mu}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\mu}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\mu}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\text{h}^{0}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\text{h}^{0}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\text{h}^{0}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\gamma}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\gamma}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\gamma}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark coupling strength.
The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark coupling strength.
The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark coupling strength.
The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark coupling strength.
The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark coupling strength.
The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark coupling strength.
The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the dark coupling strength.
The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the dark coupling strength.
The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the dark coupling strength.
The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the dark coupling strength.
The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the dark coupling strength.
The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the dark coupling strength.
Comparison of different the dijet mass $m_{\text{J}\text{J}}$, the transverse mass $m_{\text{T}}$, and the Monte Carlo (MC) mass $m_{\text{MC}}$ for a signal model with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. No selection is applied, except that there must be at least two jets. $m_{\text{MC}}$ is computed by adding the generator-level four-vectors for invisible particles to the dijet system, to represent the achievable resolution if the invisible component were fully measured. The last bin of each histogram includes the overflow events.
Comparison of different the dijet mass $m_{\text{J}\text{J}}$, the transverse mass $m_{\text{T}}$, and the Monte Carlo (MC) mass $m_{\text{MC}}$ for a signal model with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. No selection is applied, except that there must be at least two jets. $m_{\text{MC}}$ is computed by adding the generator-level four-vectors for invisible particles to the dijet system, to represent the achievable resolution if the invisible component were fully measured. The last bin of each histogram includes the overflow events.
Comparison of different the dijet mass $m_{\text{J}\text{J}}$, the transverse mass $m_{\text{T}}$, and the Monte Carlo (MC) mass $m_{\text{MC}}$ for a signal model with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. No selection is applied, except that there must be at least two jets. $m_{\text{MC}}$ is computed by adding the generator-level four-vectors for invisible particles to the dijet system, to represent the achievable resolution if the invisible component were fully measured. The last bin of each histogram includes the overflow events.
$m_{\text{T}}$ distributions for signal models with different $m_{\text{dark}}$ values for the high-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $m_{\text{dark}}$ values for the high-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $m_{\text{dark}}$ values for the high-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $m_{\text{dark}}$ values for the low-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $m_{\text{dark}}$ values for the low-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $m_{\text{dark}}$ values for the low-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $r_{\text{inv}}$ values for the high-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $r_{\text{inv}}$ values for the high-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $r_{\text{inv}}$ values for the high-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $r_{\text{inv}}$ values for the low-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $r_{\text{inv}}$ values for the low-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $r_{\text{inv}}$ values for the low-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $\alpha_{\text{dark}}$ values for the high-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $\alpha_{\text{dark}}$ values for the high-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $\alpha_{\text{dark}}$ values for the high-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $\alpha_{\text{dark}}$ values for the low-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $\alpha_{\text{dark}}$ values for the low-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $\alpha_{\text{dark}}$ values for the low-$R_{\text{T}}$ inclusive signal region.
The proportions of each SM background process in the high-$R_{\text{T}}$ signal region.
The proportions of each SM background process in the high-$R_{\text{T}}$ signal region.
The proportions of each SM background process in the high-$R_{\text{T}}$ signal region.
The proportions of each SM background process in the low-$R_{\text{T}}$ signal region.
The proportions of each SM background process in the low-$R_{\text{T}}$ signal region.
The proportions of each SM background process in the low-$R_{\text{T}}$ signal region.
The proportions of each SM background process in the high-SVJ2 signal region.
The proportions of each SM background process in the high-SVJ2 signal region.
The proportions of each SM background process in the high-SVJ2 signal region.
The proportions of each SM background process in the low-SVJ2 signal region.
The proportions of each SM background process in the low-SVJ2 signal region.
The proportions of each SM background process in the low-SVJ2 signal region.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the dark hadron mass.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the dark hadron mass.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the dark hadron mass.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the invisible fraction.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the invisible fraction.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the invisible fraction.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the dark hadron mass.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the dark hadron mass.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the dark hadron mass.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the invisible fraction.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the invisible fraction.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the invisible fraction.
Relative efficiencies in % for each step of the event selection process for the major background processes. Statistical uncertainties, at most 1.8%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for the major background processes. Statistical uncertainties, at most 1.8%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for the major background processes. Statistical uncertainties, at most 1.8%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.5%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.5%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.5%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 2.6%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 2.6%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 2.6%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 1.2%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 1.2%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 1.2%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.9%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.9%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.9%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Measurements of the associated production of a W boson and a charm (c) quark in proton-proton collisions at a centre-of-mass energy of 8 TeV are reported. The analysis uses a data sample corresponding to a total integrated luminosity of 19.7 fb$^{-1}$ collected by the CMS detector at the LHC. The W bosons are identified through their leptonic decays to an electron or a muon, and a neutrino. Charm quark jets are selected using distinctive signatures of charm hadron decays. The product of the cross section and branching fraction $\sigma$(pp $\to$ W + c + X) $\mathcal{B}$(W $\to$$\ell\nu$), where $\ell$ = e or $\mu$, and the cross section ratio $\sigma$(pp $\to$ W$^+$ + c + X) / $\sigma$(pp $\to$ W$^-$ + $\mathrm{\bar{c}}$ + X) are measured inclusively and differentially as functions of the pseudorapidity and of the transverse momentum of the lepton from the W boson decay. The results are compared with theoretical predictions. The impact of these measurements on the determination of the strange quark distribution is assessed.
Signal yields after background subtraction, efficiency*acceptance correction factors, and cross section measurements for the four channels (W decay to muon or electron and charm identification via muon or secondary vertex inside a jet).
Measured production cross sections $\sigma(W^+ + \overline{c})$, $\sigma(W^- + c)$ and their ratio.
Measured diferential cross sections $\sigma(W^- + c) + \sigma(W^+ + \overline{c})$ as a function of the absolute value of the pseudorapidity of the lepton from the W decay.
Measured diferential cross sections as a function of the transverse momentum of the lepton from the W decay.
Measured diferential cross sections ratio $R=\sigma(W^+ + \overline{c}) / \sigma(W^- + c)$ as a function of the absolute value of the pseudorapidity of the lepton from the W decay.
Measured diferential cross sections ratio $R=\sigma(W^+ + \overline{c}) / \sigma(W^- + c)$ as a function of the transverse momentum of the lepton from the W decay.
The associated production of a W and a Z boson is studied in final states with multiple leptons produced in proton-proton (pp) collisions at a centre-of-mass energy of 13 TeV using 137 fb$^{-1}$ of data collected with the CMS detector at the LHC. A measurement of the total inclusive production cross section yields $\sigma_{\text{tot}}$(pp $\to$ WZ) = 50.6 $\pm$ 0.8 (stat) $\pm$ 1.5 (syst) $\pm$ 1.1 (lumi) $\pm$ 0.5 (theo) pb. Measurements of the fiducial and differential cross sections for several key observables are also performed in all the final-state lepton flavour and charge compositions with a total of three charged leptons, which can be electrons or muons. All results are compared with theoretical predictions computed up to next-to-next-to-leading order in quantum chromodynamics plus next-to-leading order in electroweak theory and for various sets of parton distribution functions. The results include direct measurements of the charge asymmetry and the W and Z vector boson polarization. The first observation of longitudinally polarized W bosons in WZ production is reported. Anomalous gauge couplings are searched for, leading to new constraints on beyond-the-standard-model contributions to the WZ triple gauge coupling.
Distribution of the three leading leptons flavour in the CR-ZZ with uncertainties evaluated after the inclusive cross section fit
Distribution of the jet multiplicity in the CR-ttZ with uncertainties evaluated after the inclusive cross section fit
Distribution of the three leading leptons flavour in the CR-conv with uncertainties evaluated after the inclusive cross section fit
Distribution of the three leading leptons flavour in the SR-WZ with uncertainties evaluated after the inclusive cross section fit
Efficiency, acceptance, and proportion of events with leptonic tau decays in WZ production
WZ fiducial cross section in the four flavour exclusive and the flavour inclusive channels
WZ total cross section extrapolated from the four flavour exclusive and the flavour inclusive channels
Distribution of the total lepton charge in the SR-WZ with uncertainties evaluated after the inclusive cross section fit
W$^{+}$Z fiducial cross section in the four flavour exclusive and the flavour inclusive channels
W$^{-}$Z fiducial cross section in the four flavour exclusive and the flavour inclusive channels
WZ charge asymmetry ratio measured on each of the four flavour exclusive and the flavour inclusive channels
Distribution of the cosine of the W polarization angle times total lepton charge in the SR-WZ with uncertainties evaluated after the W polarization fit
Distribution of the cosine of the Z polarization angle in the SR-WZ with uncertainties evaluated after the Z polarization fit
Best fits to the W and Z polarization fractions
2D confidence regions at the 68, 95, and 99% CL in the $f_O^W$-$f_{L}^W-f_R^W$ plane
2D confidence regions at the 68, 95, and 99% CL in the $f_O^Z$-$f_{L}^Z-f_R^Z$ plane
Distribution of the invariant mass of the WZ system in the SR-WZ with uncertainties evaluated after the inclusive cross section fit
Best fit values and one dimensional confidence regions in several EFT coefficients obtained from the EFT fit considering both the SM interferences and purely BSM (order $\Lambda^{-2}$ and $\Lambda^{-4}$) terms
2D confidence regions at the 68, 95, and 99% CL in the $c_{www}$-$c_{w}$ plane
2D confidence regions at the 68, 95, and 99% CL in the $c_{w}$-$c_{b}$ plane
2D confidence regions at the 68, 95, and 99% CL in the $c_{www}$-$c_{w}$ plane
Best fit values and one dimensional confidence regions in several EFT coefficients obtained from the EFT fit considering only the SM-EFT interference (order $\Lambda^{-2}$) terms
Evolution of the best fit and expected and observed 95% CI for the $c_{w}$ parameter as a function of the cutoff scale
Evolution of the best fit and expected and observed 95% CI for the $c_{b}$ parameter as a function of the cutoff scale
Evolution of the best fit and expected and observed 95% CI for the $c_{www}$ parameter as a function of the cutoff scale
Evolution of the best fit and expected and observed 95% CI for the $\tilde{c}_{www}$ parameter as a function of the cutoff scale
Evolution of the best fit and expected and observed 95% CI for the $\tilde{c}_{w}$ parameter as a function of the cutoff scale
Differential cross section with respect to the transverse momentum of the Z boson
Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the $p_{T}$ of the Z boson
Response matrix for the $p_{T}$ of the Z boson obtained with POWHEG
Differential cross section with respect to the transverse momentum of the leading jet
Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the $p_{T}$ of the leading jet
Response matrix for the $p_{T}$ of the leading jet obtained with POWHEG
Differential cross section with respect to the jet multiplicity
Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the jet multiplicity
Response matrix for the jet multiplicity obtained with POWHEG
Differential cross section with respect to the invariant mass of the WZ system
Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the invariant mass of the WZ system
Response matrix for the invariant mass of the WZ system obtained with POWHEG
Differential cross section with respect to the transverse momentum of the lepton associated to the W boson
Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the $p_{T}$ of the lepton associated to the W boson
Response matrix for the $p_{T}$ of the lepton associated to the W boson obtained with POWHEG
Differential cross section with respect to the transverse momentum of the lepton associated to the W boson, W$^{+}$Z only
Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the $p_{T}$ of the lepton associated to the W boson, W$^{+}$Z only
Differential cross section with respect to the transverse momentum of the lepton associated to the W boson, W$^{-}$Z only
Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the $p_{T}$ of the lepton associated to the W boson, W$^{-}$Z only
Differential cross section with respect to the cosine of the W polarization angle times total lepton charge
Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the W polarization angle times total lepton charge
Response matrix for the cosine of the W polarization angle times total lepton charge obtained with POWHEG
Differential cross section with respect to the cosine of the W polarization angle times total lepton charge, W$^{+}$Z only
Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the W polarization angle times total lepton charge, W$^{+}$Z only
Differential cross section with respect to the cosine of the W polarization angle times total lepton charge, W$^{-}$Z only
Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the W polarization angle times total lepton charge, W$^{-}$Z only
Differential cross section with respect to the cosine of the Z polarization angle
Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the Z polarization angle
Response matrix for the cosine of the Z polarization angle obtained with POWHEG
Differential cross section with respect to the cosine of the Z polarization angle, W$^{+}$Z only
Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the Z polarization angle, W$^{+}$Z only
Differential cross section with respect to the cosine of the Z polarization angle, W$^{-}$Z only
Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the Z polarization angle, W$^{-}$Z only
A search is presented for new particles produced at the LHC in proton-proton collisions at $\sqrt{s} =$ 13 TeV, using events with energetic jets and large missing transverse momentum. The analysis is based on a data sample corresponding to an integrated luminosity of 101 fb$^{-1}$, collected in 2017-2018 with the CMS detector. Machine learning techniques are used to define separate categories for events with narrow jets from initial-state radiation and events with large-radius jets consistent with a hadronic decay of a W or Z boson. A statistical combination is made with an earlier search based on a data sample of 36 fb$^{-1}$, collected in 2016. No significant excess of events is observed with respect to the standard model background expectation determined from control samples in data. The results are interpreted in terms of limits on the branching fraction of an invisible decay of the Higgs boson, as well as constraints on simplified models of dark matter, on first-generation scalar leptoquarks decaying to quarks and neutrinos, and on models with large extra dimensions. Several of the new limits, specifically for spin-1 dark matter mediators, pseudoscalar mediators, colored mediators, and leptoquarks, are the most restrictive to date.
Differential signal yields for various signal hypotheses.
Differential signal yields for various signal hypotheses.
Differential signal yields for various signal hypotheses.
Differential signal yields for various signal hypotheses.
Differential signal yields for various signal hypotheses.
Differential signal yields for various signal hypotheses.
Differential signal yields for various signal hypotheses.
Differential signal yields for various signal hypotheses.
Differential signal yields for various signal hypotheses.
Differential signal yields for various signal hypotheses.
Differential signal yields for various signal hypotheses.
Differential signal yields for various signal hypotheses.
Differential signal yields for various signal hypotheses.
Differential signal yields for various signal hypotheses.
Differential signal yields for various signal hypotheses.
Differential signal yields for various signal hypotheses.
Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.
Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.
Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.
Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.
Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.
Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.
Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.
Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.
Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.
Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.
Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.
Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.
Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.
Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.
Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.
Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.
Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.
Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.
Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.
Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.
Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.
Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.
Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.
Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.
Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.
Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.
Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.
Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.
Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.
Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.
Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.
Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.
Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.
Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.
Upper limits on the coupling $g_{\chi}$ in the simplified model with a axial mediator.
Upper limits on the coupling $g_{\chi}$ in the simplified model with a axial mediator.
Upper limits on the coupling $g_{q}$ in the simplified model with a axial mediator.
Upper limits on the coupling $g_{q}$ in the simplified model with a axial mediator.
Upper limits on the coupling $g_{\chi}$ in the simplified model with a vector mediator.
Upper limits on the coupling $g_{\chi}$ in the simplified model with a vector mediator.
Upper limits on the coupling $g_{q}$ in the simplified model with a vector mediator.
Upper limits on the coupling $g_{q}$ in the simplified model with a vector mediator.
Exclusion limits on the signal strength in the simplified model with scalar couplings.
Exclusion limits on the signal strength in the simplified model with scalar couplings.
Exclusion limits on the signal strength in the simplified model with pseudoscalar couplings.
Exclusion limits on the signal strength in the simplified model with pseudoscalar couplings.
Exclusion limits on the fundamental Planck scale $M_{D}$ as a function of the number of extra dimensions $d$.
Exclusion limits on the fundamental Planck scale $M_{D}$ as a function of the number of extra dimensions $d$.
Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.
Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.
Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.
Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.
Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.
Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.
Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.
Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.
Tagging efficiency for AK8 jets. The efficiency includes the effect of the machine-learning based DeepAK8 tagger, as well as the application of the mass window requirement on the jet. The efficiency is split depending on the matching generator-level object. For reinterpretation purposes, this efficiency can directly be applied to any AK8 jet that is matched to a given type of generator-level object, with no prior selection on the jet mass. Other acceptance requirements on jet $\p_{T}$ and $\eta$ should still be applied.
Tagging efficiency for AK8 jets. The efficiency includes the effect of the machine-learning based DeepAK8 tagger, as well as the application of the mass window requirement on the jet. The efficiency is split depending on the matching generator-level object. For reinterpretation purposes, this efficiency can directly be applied to any AK8 jet that is matched to a given type of generator-level object, with no prior selection on the jet mass. Other acceptance requirements on jet $\p_{T}$ and $\eta$ should still be applied.
A search for long-lived particles (LLPs) produced in decays of standard model (SM) Higgs bosons is presented. The data sample consists of 137 fb$^{-1}$ of proton-proton collisions at $\sqrt{s} =$ 13 TeV, recorded at the LHC in 2016-2018. A novel technique is employed to reconstruct decays of LLPs in the endcap muon detectors. The search is sensitive to a broad range of LLP decay modes and to masses as low as a few GeV. No excess of events above the SM background is observed. The most stringent limits to date on the branching fraction of the Higgs boson to LLPs subsequently decaying to quarks and $\tau^+\tau^-$ are found for proper decay lengths greater than 6, 20, and 40 m, for LLP masses of 7, 15, and 40 GeV, respectively.
The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 7 GeV mass and $ S \rightarrow d\bar{d}$ decay mode.
The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 7 GeV mass and $ S \rightarrow d\bar{d}$ decay mode.
The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 15 GeV mass and $ S \rightarrow d\bar{d}$ decay mode.
The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 15 GeV mass and $ S \rightarrow d\bar{d}$ decay mode.
The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 40 GeV mass and $ S \rightarrow d\bar{d}$ decay mode.
The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 40 GeV mass and $ S \rightarrow d\bar{d}$ decay mode.
The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 55 GeV mass and $ S \rightarrow d\bar{d}$ decay mode.
The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 55 GeV mass and $ S \rightarrow d\bar{d}$ decay mode.
The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 7 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.
The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 7 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.
The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 15 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.
The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 15 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.
The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 40 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.
The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 40 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.
The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 55 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.
The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 55 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.
The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 15 GeV mass and $ S \rightarrow b\bar{b}$ decay mode.
The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 15 GeV mass and $ S \rightarrow b\bar{b}$ decay mode.
The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 40 GeV mass and $ S \rightarrow b\bar{b}$ decay mode.
The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 40 GeV mass and $ S \rightarrow b\bar{b}$ decay mode.
The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 55 GeV mass and $ S \rightarrow b\bar{b}$ decay mode.
The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 55 GeV mass and $ S \rightarrow b\bar{b}$ decay mode.
The cluster efficiency in bins of hadronic and EM energy in region A. Region A is defined as 391 cm $< r <$ 695.5 cm and 400 cm $< |z| <$ 671 cm. The cluster efficiency is estimated with LLPs decaying to $\tau^{+} \tau^{-}$. The sample contains equal fractions of events with LLP mass of 7, 15, 40, and 55 GeV and LLP lifetime of 0.1, 1, 10, and 100m. The first hadronic energy bins correspond to LLPs that decayed leptonically with 0 hadronic energy. The cluster efficiency includes all cluster-level selections described in the paper, except for the jet veto, time cut, and $\Delta\phi$ cut. The full simulation signal yield prediction for samples with various LLP mass between 7 - 55 GeV, lifetime between 0.1 - 100 m, and decay mode to $d\bar{d}$ and $\tau^{+} \tau^{-}$ can be reproduced using this parameterization to within 35% and 20% for region A and B, respectively.
The cluster efficiency in bins of hadronic and EM energy in region A. Region A is defined as 391 cm $< r <$ 695.5 cm and 400 cm $< |z| <$ 671 cm. The cluster efficiency is estimated with LLPs decaying to $\tau^{+} \tau^{-}$. The sample contains equal fractions of events with LLP mass of 7, 15, 40, and 55 GeV and LLP lifetime of 0.1, 1, 10, and 100m. The first hadronic energy bins correspond to LLPs that decayed leptonically with 0 hadronic energy. The cluster efficiency includes all cluster-level selections described in the paper, except for the jet veto, time cut, and $\Delta\phi$ cut. The full simulation signal yield prediction for samples with various LLP mass between 7 - 55 GeV, lifetime between 0.1 - 100 m, and decay mode to $d\bar{d}$ and $\tau^{+} \tau^{-}$ can be reproduced using this parameterization to within 35% and 20% for region A and B, respectively.
The cluster efficiency in bins of hadronic and EM energy in region B. Region B is defined as 671 cm $< |z| <$ 1100 cm, $r <$ 695.5 cm, and $|\eta| <$ 2. The cluster efficiency is estimated with LLPs decaying to $\tau^{+} \tau^{-}$. The sample contains equal fractions of events with LLP mass of 7, 15, 40, and 55 GeV and LLP lifetime of 0.1, 1, 10, and 100m. The first hadronic energy bins correspond to LLPs that decayed leptonically with 0 hadronic energy. The cluster efficiency includes all cluster-level selections described in the paper, except for the jet veto, time cut, and $\Delta\phi$ cut. The full simulation signal yield prediction for samples with various LLP mass between 7 - 55 GeV, lifetime between 0.1 - 100 m, and decay mode to $d\bar{d}$ and $\tau^{+} \tau^{-}$ can be reproduced using this parameterization to within 35% and 20% for region A and B, respectively.
The cluster efficiency in bins of hadronic and EM energy in region B. Region B is defined as 671 cm $< |z| <$ 1100 cm, $r <$ 695.5 cm, and $|\eta| <$ 2. The cluster efficiency is estimated with LLPs decaying to $\tau^{+} \tau^{-}$. The sample contains equal fractions of events with LLP mass of 7, 15, 40, and 55 GeV and LLP lifetime of 0.1, 1, 10, and 100m. The first hadronic energy bins correspond to LLPs that decayed leptonically with 0 hadronic energy. The cluster efficiency includes all cluster-level selections described in the paper, except for the jet veto, time cut, and $\Delta\phi$ cut. The full simulation signal yield prediction for samples with various LLP mass between 7 - 55 GeV, lifetime between 0.1 - 100 m, and decay mode to $d\bar{d}$ and $\tau^{+} \tau^{-}$ can be reproduced using this parameterization to within 35% and 20% for region A and B, respectively.
The efficiency of $N_{station} > 1$ requirement in bins of hadronic energy in region B. Region B is defined as 671 cm $< |z| <$ 1100 cm, $r <$ 695.5 cm, and $|\eta| <$ 2. The cluster efficiency is estimated with LLPs decaying to $\tau^{+} \tau^{-}$. The sample contains equal fractions of events with LLP mass of 7, 15, 40, and 55 GeV and LLP lifetime of 0.1, 1, 10, and 100m. The first hadronic energy bin corresponds to LLPs that decayed leptonically with 0 hadronic energy. The efficiency is calculated with respect to clusters that pass all cluster-level cuts described in the paper, except for the jet veto, time cut, and $\Delta\phi$ cut. The full simulation signal yield prediction for samples with various LLP mass between 7 - 55 GeV, lifetime between 0.1 - 100 m, and decay mode to $d\bar{d}$ and $\tau^{+} \tau^{-}$ can be reproduced using this parameterization to within 10%.
The efficiency of $N_{station} > 1$ requirement in bins of hadronic energy in region B. Region B is defined as 671 cm $< |z| <$ 1100 cm, $r <$ 695.5 cm, and $|\eta| <$ 2. The cluster efficiency is estimated with LLPs decaying to $\tau^{+} \tau^{-}$. The sample contains equal fractions of events with LLP mass of 7, 15, 40, and 55 GeV and LLP lifetime of 0.1, 1, 10, and 100m. The first hadronic energy bin corresponds to LLPs that decayed leptonically with 0 hadronic energy. The efficiency is calculated with respect to clusters that pass all cluster-level cuts described in the paper, except for the jet veto, time cut, and $\Delta\phi$ cut. The full simulation signal yield prediction for samples with various LLP mass between 7 - 55 GeV, lifetime between 0.1 - 100 m, and decay mode to $d\bar{d}$ and $\tau^{+} \tau^{-}$ can be reproduced using this parameterization to within 10%.
The geometric signal acceptance as a function of $c\tau$. The acceptance is shown for four different LLP mass hypotheses: 7, 15, 40, and 55 GeV. The acceptance is defined by requiring at least 1 LLP to decay in the region defined as 400 cm $< |z| <$ 1100 cm, $r < $ 695.5 cm, and $|\eta| <$ 2.4.
A search for production of the supersymmetric partners of the top quark, top squarks, is presented. The search is based on proton-proton collision events containing multiple jets, no leptons, and large transverse momentum imbalance. The data were collected with the CMS detector at the CERN LHC at a center-of-mass energy of 13 TeV, and correspond to an integrated luminosity of 137 fb$^{-1}$. The targeted signal production scenarios are direct and gluino-mediated top squark production, including scenarios in which the top squark and neutralino masses are nearly degenerate. The search utilizes novel algorithms based on deep neural networks that identify hadronically decaying top quarks and W bosons, which are expected in many of the targeted signal models. No statistically significant excess of events is observed relative to the expectation from the standard model, and limits on the top squark production cross section are obtained in the context of simplified supersymmetric models for various production and decay modes. Exclusion limits as high as 1310 GeV are established at the 95% confidence level on the mass of the top squark for direct top squark production models, and as high as 2260 GeV on the mass of the gluino for gluino-mediated top squark production models. These results represent a significant improvement over the results of previous searches for supersymmetry by CMS in the same final state.
Top quark tagging efficiencies are shown as a function of the generator-level top quark $p_T$ for the merged tagging algorithm and resolved tagging algorithm described in the paper. This plot shows the efficiencies as calculated in a sample of simulated $t\bar{t}$ events in which one top quark decays leptonically, while the other decays hadronically. In addition to the individual algorithms shown as orange squares (boosted top quarks) and green inverted triangles (resolved top quarks), the total top quark tagging efficiency (blue dots) is also shown.
W boson tagging efficiencies are shown as a function of the generator-level W boson $p_T$ for the merged tagging algorithm described in the paper. This plot shows the W boson tagging efficiency when calculated in a sample of simulated WW events.
Comparison between data and simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $p_T^{miss}$ after scaling the simulation to match the total yield in data. The hatched region indicates the total shape uncertainty in the simulation.
The ratio between the observed data and the simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $p_T^{miss}$ after scaling the simulation to match the total yield in data.
Comparison between data and simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_t$ after scaling the simulation to match the total yield in data. The hatched region indicates the total shape uncertainty in the simulation.
The ratio between the observed data and the simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_t$ after scaling the simulation to match the total yield in data.
Comparison between data and simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_W$ after scaling the simulation to match the total yield in data. The hatched region indicates the total shape uncertainty in the simulation.
The ratio between the observed data and the simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_W$ after scaling the simulation to match the total yield in data.
Comparison between data and simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_{\text{res}}$ after scaling the simulation to match the total yield in data. The hatched region indicates the total shape uncertainty in the simulation.
The ratio between the observed data and the simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_{\text{res}}$ after scaling the simulation to match the total yield in data.
Observed event yields in data (black points) and predicted SM background (filled histograms) for the low $\Delta$m search bins 0--52. The signal models are denoted in the legend with the masses in GeV of the SUSY particles in parentheses: $(m_{\tilde{t}}, m_{\tilde{\chi}^0_1})$ or $(m_{\tilde{g}}, m_{\tilde{\chi}^0_1})$ for the T2 or T1 signal models, respectively. The hatched bands correspond to the total uncertainty in the background prediction. The (unstacked) distributions for two example signal models are also shown.
The ratio of the data to the total background prediction for the low $\Delta$m search bins 0--52. The hatched bands correspond to the total uncertainty in the background prediction.
Observed event yields in data (black points) and predicted SM background (filled histograms) for the high $\Delta$m search bins 53--104. The signal models are denoted in the legend with the masses in GeV of the SUSY particles in parentheses: $(m_{\tilde{t}}, m_{\tilde{\chi}^0_1})$ or $(m_{\tilde{g}}, m_{\tilde{\chi}^0_1})$ for the T2 or T1 signal models, respectively. The hatched bands correspond to the total uncertainty in the background prediction. The (unstacked) distributions for two example signal models are also shown.
The ratio of the data to the total background prediction for the high $\Delta$m search bins 53--104. The hatched bands correspond to the total uncertainty in the background prediction.
Observed event yields in data (black points) and predicted SM background (filled histograms) for the high $\Delta$m search bins 105--152 with ${N_b = 2}$. The signal models are denoted in the legend with the masses in GeV of the SUSY particles in parentheses: $(m_{\tilde{t}}, m_{\tilde{\chi}^0_1})$ or $(m_{\tilde{g}}, m_{\tilde{\chi}^0_1})$ for the T2 or T1 signal models, respectively. The hatched bands correspond to the total uncertainty in the background prediction. The (unstacked) distributions for two example signal models are also shown.
The ratio of the data to the total background prediction for the high $\Delta$m search bins 105--152 with ${N_b = 2}$. The hatched bands correspond to the total uncertainty in the background prediction.
Observed event yields in data (black points) and predicted SM background (filled histograms) for the high $\Delta$m search bins 153--182 with ${N_b \geq 3}$. The signal models are denoted in the legend with the masses in GeV of the SUSY particles in parentheses: $(m_{\tilde{t}}, m_{\tilde{\chi}^0_1})$ or $(m_{\tilde{g}}, m_{\tilde{\chi}^0_1})$ for the T2 or T1 signal models, respectively. The hatched bands correspond to the total uncertainty in the background prediction. The (unstacked) distributions for two example signal models are also shown.
The ratio of the data to the total background prediction for the high $\Delta$m search bins 153--182 with ${N_b \geq 3}$. The hatched bands correspond to the total uncertainty in the background prediction.
The observed 95% CL upper limit on the production cross section of the T2tt simplified model as a function of the top squark and LSP masses. No interpretation is provided for signal models for which ${|{m_{\tilde{t}} - m_{\tilde{\chi}^0_1} - m_t}| < 25 GeV}$ and ${m_{\tilde{t}} < 275 GeV}$ as described in the text.
The expected 95% CL upper limit on the production cross section of the T2tt simplified model as a function of the top squark and LSP masses. No interpretation is provided for signal models for which ${|{m_{\tilde{t}} - m_{\tilde{\chi}^0_1} - m_t}| < 25 GeV}$ and ${m_{\tilde{t}} < 275 GeV}$ as described in the text.
The observed exclusion contour of the T2tt simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$). No interpretation is provided for signal models for which ${|{m_{\tilde{t}} - m_{\tilde{\chi}^0_1} - m_t}| < 25 GeV}$ and ${m_{\tilde{t}} < 275 GeV}$ as described in the text.
The mean expected exclusion contour of the T2tt simplified model and the region containing 68 and 95\% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis. No interpretation is provided for signal models for which ${|{m_{\tilde{t}} - m_{\tilde{\chi}^0_1} - m_t}| < 25 GeV}$ and ${m_{\tilde{t}} < 275 GeV}$ as described in the text.
The observed 95% CL upper limit on the production cross section of the T2bW simplified model as a function of the top squark and LSP masses.
The expected 95% CL upper limit on the production cross section of the T2bW simplified model as a function of the top squark and LSP masses.
The observed exclusion contour of the T2bW simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).
The mean expected exclusion contour of the T2bW simplified model and the region containing 68 and 95\% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.
The observed 95% CL upper limit on the production cross section of the T2tb simplified model as a function of the top squark and LSP masses.
The expected 95% CL upper limit on the production cross section of the T2tb simplified model as a function of the top squark and LSP masses.
The observed exclusion contour of the T2tb simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).
The mean expected exclusion contour of the T2tb simplified model and the region containing 68 and 95\% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.
The observed 95% CL upper limit on the production cross section of the T2ttC simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.
The expected 95% CL upper limit on the production cross section of the T2ttC simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.
The observed exclusion contour of the T2ttC simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).
The mean expected exclusion contour of the T2ttC simplified model and the region containing 68\% ($\pm 1\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.
The observed 95% CL upper limit on the production cross section of the T2bWC simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.
The expected 95% CL upper limit on the production cross section of the T2bWC simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.
The observed exclusion contour of the T2bWC simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).
The mean expected exclusion contour of the T2bWC simplified model and the region containing 68\% ($\pm 1\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.
The observed 95% CL upper limit on the production cross section of the T2cc simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.
The expected 95% CL upper limit on the production cross section of the T2cc simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.
The observed exclusion contour of the T2cc simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).
The mean expected exclusion contour of the T2cc simplified model and the region containing 68\% ($\pm 1\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.
The observed 95% CL upper limit on the production cross section of the T1tttt simplified model as a function of the gluino and LSP masses.
The expected 95% CL upper limit on the production cross section of the T1tttt simplified model as a function of the gluino and LSP masses.
The observed exclusion contour of the T1tttt simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).
The mean expected exclusion contour of the T1tttt simplified model and the region containing 68 and 95\% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.
The observed 95% CL upper limit on the production cross section of the T1ttbb simplified model as a function of the gluino and LSP masses.
The expected 95% CL upper limit on the production cross section of the T1ttbb simplified model as a function of the gluino and LSP masses.
The observed exclusion contour of the T1ttbb simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).
The mean expected exclusion contour of the T1ttbb simplified model and the region containing 68 and 95\% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.
The observed 95% CL upper limit on the production cross section of the T5ttcc simplified model as a function of the gluino and LSP masses. The upper limits do not take into account contributions from direct top squark pair production; however, its effect is small for $m_{\tilde{\chi}^0_1} > 600 GeV$, which corresponds to the phase space beyond the exclusions based on direct top squark pair production. The excluded regions based on direct top squark pair production from this search and earlier searches are indicated by the hatched areas.
The expected 95% CL upper limit on the production cross section of the T5ttcc simplified model as a function of the gluino and LSP masses. The uppser limits do not take into account contributions from direct top squark pair production; however, its effect is small for $m_{\tilde{\chi}^0_1} > 600 GeV$, which corresponds to the phase space beyond the exclusions based on direct top squark pair production. The excluded regions based on direct top squark pair production from this search and earlier searches are indicated by the hatched areas.
The observed exclusion contour of the T5ttcc simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$). The expected and observed upper limits do not take into account contributions from direct top squark pair production; however, its effect is small for $m_{\tilde{\chi}^0_1} > 600 GeV$, which corresponds to the phase space beyond the exclusions based on direct top squark pair production. The excluded regions based on direct top squark pair production from this search and earlier searches are indicated by the hatched areas.
The mean expected exclusion contour of the T5ttcc simplified model and the region containing 68% and 95% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis. The expected and observed upper limits do not take into account contributions from direct top squark pair production; however, its effect is small for $m_{\tilde{\chi}^0_1} > 600 GeV$, which corresponds to the phase space beyond the exclusions based on direct top squark pair production. The excluded regions based on direct top squark pair production from this search and earlier searches are indicated by the hatched areas.
A search is presented for a heavy vector resonance decaying into a Z boson and the standard model Higgs boson, where the Z boson is identified through its leptonic decays to electrons, muons, or neutrinos, and the Higgs boson is identified through its hadronic decays. The search is performed in a Lorentz-boosted regime and is based on data collected from 2016 to 2018 at the CERN LHC, corresponding to an integrated luminosity of 137 fb$^{-1}$. Upper limits are derived on the production of a narrow heavy resonance Z', and a mass below 3.5 and 3.7 TeV is excluded at 95% confidence level in models where the heavy vector boson couples exclusively to fermions and to bosons, respectively. These are the most stringent limits placed on the Heavy Vector Triplet Z' model to date. If the heavy vector boson couples exclusively to standard model bosons, upper limits on the product of the cross section and branching fraction are set between 23 and 0.3 fb for a Z' mass between 0.8 and 4.6 TeV, respectively. This is the first limit set on a heavy vector boson coupling exclusively to standard model bosons in its production and decay.
The product of signal acceptance and efficiency in the 0l categories for the signal produced via qqbar annihilation.
The product of signal acceptance and efficiency in the 2l categories for the signal produced via qqbar annihilation.
The product of signal acceptance and efficiency in the 0l categories for the signal produced via vector boson fusion.
The product of signal acceptance and efficiency in the 2l categories for the signal produced via vector boson fusion.
$m_{X}^{T}$ distribution in data in the 0l 2b non-VBF category. The data is shown as Events/10 GeV. The distribution is shown up 4000 GeV, which corresponds to the event with the highest $m_{X}^{T}$ observed in the SR.
$m_{X}^{T}$ distribution in data in the 0l $\leq$1b non-VBF category. The data is shown as Events/10 GeV. The distribution is shown up 4000 GeV, which corresponds to the event with the highest $m_{X}^{T}$ observed in the SR.
$m_{X}$ distribution in data in the 2e 2b non-VBF category. The data is shown as Events/10 GeV. The distribution is shown up 4000 GeV, which corresponds to the event with the highest $m_{X}$ observed in the SR.
$m_{X}$ distribution in data in the 2e $\leq$1b non-VBF category. The data is shown as Events/10 GeV. The distribution is shown up 4000 GeV, which corresponds to the event with the highest $m_{X}$ observed in the SR.
$m_{X}$ distribution in data in the 2$\mu$ 2b non-VBF category. The data is shown as Events/10 GeV. The distribution is shown up 4000 GeV, which corresponds to the event with the highest $m_{X}$ observed in the SR.
$m_{X}$ distribution in data in the 2$\mu$ $\leq$1b non-VBF category. The data is shown as Events/10 GeV. The distribution is shown up 4000 GeV, which corresponds to the event with the highest $m_{X}$ observed in the SR.
$m_{X}^{T}$ distribution in data in the 0l 2b VBF category. The data is shown as Events/10 GeV. The distribution is shown up 4000 GeV, which corresponds to the event with the highest $m_{X}^{T}$ observed in the SR.
$m_{X}^{T}$ distribution in data in the 0l $\leq$1b VBF category. The data is shown as Events/10 GeV. The distribution is shown up 4000 GeV, which corresponds to the event with the highest $m_{X}^{T}$ observed in the SR.
$m_{X}$ distribution in data in the 2e 2b VBF category. The data is shown as Events/10 GeV. The distribution is shown up 4000 GeV, which corresponds to the event with the highest $m_{X}$ observed in the SR.
$m_{X}$ distribution in data in the 2e $\leq$1b VBF category. The data is shown as Events/10 GeV. The distribution is shown up 4000 GeV, which corresponds to the event with the highest $m_{X}$ observed in the SR.
$m_{X}$ distribution in data in the 2$\mu$ 2b VBF category. The data is shown as Events/10 GeV. The distribution is shown up 4000 GeV, which corresponds to the event with the highest $m_{X}$ observed in the SR.
$m_{X}$ distribution in data in the 2$\mu$ $\leq$1b VBF category. The data is shown as Events/10 GeV. The distribution is shown up 4000 GeV, which corresponds to the event with the highest $m_{X}$ observed in the SR.
Observed and expected 95% CL upper limit on $\sigma \mathcal{B}$(Z'-> ZH) with all categories combined for the non-VBF signal, including all statistical and systematic uncertainties. The inner green band and the outer yellow band indicate the regions containing 68 and 95%, respectively, of the distribution of expected limits under the background-only hypothesis. The CMS search for a heavy resonance using 2016 data and the same final state [JHEP 11 (2018) 172] is shown as a comparison.
Observed and expected 95% CL upper limit on $\sigma \mathcal{B}$(Z'-> ZH) with all categories combined for the VBF signal, including all statistical and systematic uncertainties. The inner green band and the outer yellow band indicate the regions containing 68 and 95%, respectively, of the distribution of expected limits under the background-only hypothesis.
Observed exclusion limit in the space of the HVT model parameters [$g_{V}c_{H}$, $g^{2}c_{F}/g_{V}$] for mass hypotheses of 2 TeV for the non-VBF signal.
Observed exclusion limit in the space of the HVT model parameters [$g_{V}c_{H}$, $g^{2}c_{F}/g_{V}$] for mass hypotheses of 3 TeV for the non-VBF signal.
Observed exclusion limit in the space of the HVT model parameters [$g_{V}c_{H}$, $g^{2}c_{F}/g_{V}$] for mass hypotheses of 4 TeV for the non-VBF signal.
A search is performed for the rare decay W$^\pm\to\pi^\pm\gamma$ in proton-proton collisions at $\sqrt{s} =$ 13 TeV. Data corresponding to an on W integrated luminosity of 137 fb$^{-1}$ were collected during 2016 to 2018 with the CMS detector. This analysis exploits a novel search strategy based on W boson production in top quark pair events. An inclusive search for the W$^\pm\to\pi^\pm\gamma$ decay is not optimal at the LHC because of the high trigger thresholds. Instead, a trigger selection is exploited in which the W boson originating from one of the top quarks is used to tag the event in a leptonic decay. The W boson emerging from the other top quark is used to search for the W$^\pm\to\pi^\pm\gamma$ signature. Such decays are characterized by an isolated track pointing to a large energy deposit, and by an isolated photon of large transverse momentum. The presence of b quark jets reduces the background from the hadronization of light-flavor quarks and gluons. The W$^\pm\to\pi^\pm\gamma$ decay is not observed. An upper exclusion limit is set to this branching fraction, corresponding to 1.50 $\times$ 10$^{-5}$ at 95% confidence level, whereas the expected upper limit exclusion limit is 0.85 $^{+0.52}_{-0.29}$ $\times$ 10$^{-5}$.
The product of signal efficiency and acceptance per year and per lepton channel (muon or electron).
Expected and observed upper exclusion limits on the branching fraction of the decay of a W boson into a pion and a photon, using 2016 to 2018 data.
A search is described for the production of a pair of bottom-type vector-like quarks (VLQs), each decaying into a b or $\mathrm{\bar{b}}$ quark and either a Higgs or a Z boson, with a mass greater than 1000 GeV. The analysis is based on data from proton-proton collisions at a 13 TeV center-of-mass energy recorded at the CERN LHC, corresponding to a total integrated luminosity of 137 fb$^{-1}$. As the predominant decay modes of the Higgs and Z bosons are to a pair of quarks, the analysis focuses on final states consisting of jets resulting from the six quarks produced in the events. Since the two jets produced in the decay of a highly Lorentz-boosted Higgs or Z boson can merge to form a single jet, nine independent analyses are performed, categorized by the number of observed jets and the reconstructed event mode. No signal in excess of the expected background is observed. Lower limits are set on the VLQ mass at 95% confidence level equal to 1570 GeV in the case where the VLQ decays exclusively to a b quark and a Higgs boson, 1390 GeV for when it decays exclusively to a b quark and a Z boson, and 1450 GeV for when it decays equally in these two modes. These limits represent significant improvements over the previously published VLQ limits.
Measured values of the trigger efficiencies for events with $\HT > 1350\GeV$. The uncertainties are statistical only.
Reconstructed VLQ mass distributions for simulated signal events with a generated VLQ mass $m_{B} = 1200\GeV$. A moderate requirement of $\chi^{2}$/ndf < 2$ is applied to the events. Mass distributions for 4-jet (left), 5-jet (center), and 6-jet (right) events are shown for the three decay modes: bHbH (upper row), bHbZ (middle row), and bZbZ (lower row).
Reconstructed VLQ mass distributions for simulated signal events with a generated VLQ mass $m_{B} = 1200\GeV$. A moderate requirement of $\chi^{2}$/ndf < 2$ is applied to the events. Mass distributions for 4-jet (left), 5-jet (center), and 6-jet (right) events are shown for the three decay modes: bHbH (upper row), bHbZ (middle row), and bZbZ (lower row).
Reconstructed VLQ mass distributions for simulated signal events with a generated VLQ mass $m_{B} = 1200\GeV$. A moderate requirement of $\chi^{2}$/ndf < 2$ is applied to the events. Mass distributions for 4-jet (left), 5-jet (center), and 6-jet (right) events are shown for the three decay modes: bHbH (upper row), bHbZ (middle row), and bZbZ (lower row).
Reconstructed VLQ mass distributions for simulated signal events with a generated VLQ mass $m_{B} = 1200\GeV$. A moderate requirement of $\chi^{2}$/ndf < 2$ is applied to the events. Mass distributions for 4-jet (left), 5-jet (center), and 6-jet (right) events are shown for the three decay modes: bHbH (upper row), bHbZ (middle row), and bZbZ (lower row).
Reconstructed VLQ mass distributions for simulated signal events with a generated VLQ mass $m_{B} = 1200\GeV$. A moderate requirement of $\chi^{2}$/ndf < 2$ is applied to the events. Mass distributions for 4-jet (left), 5-jet (center), and 6-jet (right) events are shown for the three decay modes: bHbH (upper row), bHbZ (middle row), and bZbZ (lower row).
Reconstructed VLQ mass distributions for simulated signal events with a generated VLQ mass $m_{B} = 1200\GeV$. A moderate requirement of $\chi^{2}$/ndf < 2$ is applied to the events. Mass distributions for 4-jet (left), 5-jet (center), and 6-jet (right) events are shown for the three decay modes: bHbH (upper row), bHbZ (middle row), and bZbZ (lower row).
Reconstructed VLQ mass distributions for simulated signal events with a generated VLQ mass $m_{B} = 1200\GeV$. A moderate requirement of $\chi^{2}$/ndf < 2$ is applied to the events. Mass distributions for 4-jet (left), 5-jet (center), and 6-jet (right) events are shown for the three decay modes: bHbH (upper row), bHbZ (middle row), and bZbZ (lower row).
Reconstructed VLQ mass distributions for simulated signal events with a generated VLQ mass $m_{B} = 1200\GeV$. A moderate requirement of $\chi^{2}$/ndf < 2$ is applied to the events. Mass distributions for 4-jet (left), 5-jet (center), and 6-jet (right) events are shown for the three decay modes: bHbH (upper row), bHbZ (middle row), and bZbZ (lower row).
Reconstructed VLQ mass distributions for simulated signal events with a generated VLQ mass $m_{B} = 1200\GeV$. A moderate requirement of $\chi^{2}$/ndf < 2$ is applied to the events. Mass distributions for 4-jet (left), 5-jet (center), and 6-jet (right) events are shown for the three decay modes: bHbH (upper row), bHbZ (middle row), and bZbZ (lower row).
Distribution of $\chi^{2}$/ndf for the best jet combination for simulated 1200\GeV VLQ events (red histogram) and data (black points), for 4-jet events (left), 5-jet events (center), and 6-jet events (right). The simulated signal events and data events are normalized to the same value within the displayed $\chi^{2}$/ndf range.
Distribution of $\chi^{2}$/ndf for the best jet combination for simulated 1200\GeV VLQ events (red histogram) and data (black points), for 4-jet events (left), 5-jet events (center), and 6-jet events (right). The simulated signal events and data events are normalized to the same value within the displayed $\chi^{2}$/ndf range.
Distribution of $\chi^{2}$/ndf for the best jet combination for simulated 1200\GeV VLQ events (red histogram) and data (black points), for 4-jet events (left), 5-jet events (center), and 6-jet events (right). The simulated signal events and data events are normalized to the same value within the displayed $\chi^{2}$/ndf range.
Distributions of the average reconstructed mass of VLQ candidates for the jet combination with the least $\chi^{2}$ in 4-jet (left), 5-jet (center), and 6-jet (right) multiplicity events. The red lines show the exponential fit in the range 1000-2000 GeV. The lower panels show the fractional difference, (data-fit)/fit
Distributions of the average reconstructed mass of VLQ candidates for the jet combination with the least $\chi^{2}$ in 4-jet (left), 5-jet (center), and 6-jet (right) multiplicity events. The red lines show the exponential fit in the range 1000-2000 GeV. The lower panels show the fractional difference, (data-fit)/fit
Distributions of the average reconstructed mass of VLQ candidates for the jet combination with the least $\chi^{2}$ in 4-jet (left), 5-jet (center), and 6-jet (right) multiplicity events. The red lines show the exponential fit in the range 1000-2000 GeV. The lower panels show the fractional difference, (data-fit)/fit
Dependence of the BJTF on the average reconstructed VLQ mass in the control region, 12 < $\chi^{2}$/ndf < 48 for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes. The data is shown in black points, and the linear fit and its uncertainty are shown as the red line and the pale red band.
Dependence of the BJTF on the average reconstructed VLQ mass in the control region, 12 < $\chi^{2}$/ndf < 48 for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes. The data is shown in black points, and the linear fit and its uncertainty are shown as the red line and the pale red band.
Dependence of the BJTF on the average reconstructed VLQ mass in the control region, 12 < $\chi^{2}$/ndf < 48 for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes. The data is shown in black points, and the linear fit and its uncertainty are shown as the red line and the pale red band.
Dependence of the BJTF on the average reconstructed VLQ mass in the control region, 12 < $\chi^{2}$/ndf < 48 for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes. The data is shown in black points, and the linear fit and its uncertainty are shown as the red line and the pale red band.
Dependence of the BJTF on the average reconstructed VLQ mass in the control region, 12 < $\chi^{2}$/ndf < 48 for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes. The data is shown in black points, and the linear fit and its uncertainty are shown as the red line and the pale red band.
Dependence of the BJTF on the average reconstructed VLQ mass in the control region, 12 < $\chi^{2}$/ndf < 48 for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes. The data is shown in black points, and the linear fit and its uncertainty are shown as the red line and the pale red band.
Dependence of the BJTF on the average reconstructed VLQ mass in the control region, 12 < $\chi^{2}$/ndf < 48 for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes. The data is shown in black points, and the linear fit and its uncertainty are shown as the red line and the pale red band.
Dependence of the BJTF on the average reconstructed VLQ mass in the control region, 12 < $\chi^{2}$/ndf < 48 for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes. The data is shown in black points, and the linear fit and its uncertainty are shown as the red line and the pale red band.
Dependence of the BJTF on the average reconstructed VLQ mass in the control region, 12 < $\chi^{2}$/ndf < 48 for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes. The data is shown in black points, and the linear fit and its uncertainty are shown as the red line and the pale red band.
Dependence of the BJTF on $\chi^{2}$/ndf in the low-mass VLQ region, for 4-jet (left column), 5-jet, (center column) and 6-jet (right column) multiplicities and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes. The data is shown in black points, and the linear fit and its uncertainty are shown as the red line and the pale red band.
Dependence of the BJTF on $\chi^{2}$/ndf in the low-mass VLQ region, for 4-jet (left column), 5-jet, (center column) and 6-jet (right column) multiplicities and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes. The data is shown in black points, and the linear fit and its uncertainty are shown as the red line and the pale red band.
Dependence of the BJTF on $\chi^{2}$/ndf in the low-mass VLQ region, for 4-jet (left column), 5-jet, (center column) and 6-jet (right column) multiplicities and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes. The data is shown in black points, and the linear fit and its uncertainty are shown as the red line and the pale red band.
Dependence of the BJTF on $\chi^{2}$/ndf in the low-mass VLQ region, for 4-jet (left column), 5-jet, (center column) and 6-jet (right column) multiplicities and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes. The data is shown in black points, and the linear fit and its uncertainty are shown as the red line and the pale red band.
Dependence of the BJTF on $\chi^{2}$/ndf in the low-mass VLQ region, for 4-jet (left column), 5-jet, (center column) and 6-jet (right column) multiplicities and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes. The data is shown in black points, and the linear fit and its uncertainty are shown as the red line and the pale red band.
Dependence of the BJTF on $\chi^{2}$/ndf in the low-mass VLQ region, for 4-jet (left column), 5-jet, (center column) and 6-jet (right column) multiplicities and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes. The data is shown in black points, and the linear fit and its uncertainty are shown as the red line and the pale red band.
Dependence of the BJTF on $\chi^{2}$/ndf in the low-mass VLQ region, for 4-jet (left column), 5-jet, (center column) and 6-jet (right column) multiplicities and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes. The data is shown in black points, and the linear fit and its uncertainty are shown as the red line and the pale red band.
Dependence of the BJTF on $\chi^{2}$/ndf in the low-mass VLQ region, for 4-jet (left column), 5-jet, (center column) and 6-jet (right column) multiplicities and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes. The data is shown in black points, and the linear fit and its uncertainty are shown as the red line and the pale red band.
Dependence of the BJTF on $\chi^{2}$/ndf in the low-mass VLQ region, for 4-jet (left column), 5-jet, (center column) and 6-jet (right column) multiplicities and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes. The data is shown in black points, and the linear fit and its uncertainty are shown as the red line and the pale red band.
The reduction factor in data events, for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities, and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes.
The reduction factor in data events, for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities, and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes.
The reduction factor in data events, for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities, and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes.
The reduction factor in data events, for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities, and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes.
The reduction factor in data events, for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities, and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes.
The reduction factor in data events, for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities, and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes.
The reduction factor in data events, for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities, and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes.
The reduction factor in data events, for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities, and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes.
The reduction factor in data events, for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities, and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes.
The reduction factor in $m_B$ = 1200 GeV VLQ signal events, for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities, and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes.
The reduction factor in $m_B$ = 1200 GeV VLQ signal events, for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities, and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes.
The reduction factor in $m_B$ = 1200 GeV VLQ signal events, for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities, and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes.
The reduction factor in $m_B$ = 1200 GeV VLQ signal events, for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities, and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes.
The reduction factor in $m_B$ = 1200 GeV VLQ signal events, for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities, and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes.
The reduction factor in $m_B$ = 1200 GeV VLQ signal events, for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities, and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes.
The reduction factor in $m_B$ = 1200 GeV VLQ signal events, for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities, and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes.
The reduction factor in $m_B$ = 1200 GeV VLQ signal events, for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities, and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes.
The reduction factor in $m_B$ = 1200 GeV VLQ signal events, for 4-jet (left column), 5-jet (center column), and 6-jet (right column) multiplicities, and for the bHbH (upper row), bHbZ (middle row), and bZbZ (lower row) event modes.
Data (black points), expected background (solid blue histogram), and expected background plus a VLQ signal for different VLQ masses (colored lines). Left column: 4-jet events, center column: 5-jet events, right column: 6-jet events; upper row: bHbH, middle row: bHbZ,lower row: bZbZ. For the signal, a 100% B -> bH branching fraction is assumed. The hatched regions for the background and background plus signal distributions indicate the systematic uncertainties. All three data-taking years are combined.
Data (black points), expected background (solid blue histogram), and expected background plus a VLQ signal for different VLQ masses (colored lines). Left column: 4-jet events, center column: 5-jet events, right column: 6-jet events; upper row: bHbH, middle row: bHbZ,lower row: bZbZ. For the signal, a 100% B -> bH branching fraction is assumed. The hatched regions for the background and background plus signal distributions indicate the systematic uncertainties. All three data-taking years are combined.
Data (black points), expected background (solid blue histogram), and expected background plus a VLQ signal for different VLQ masses (colored lines). Left column: 4-jet events, center column: 5-jet events, right column: 6-jet events; upper row: bHbH, middle row: bHbZ,lower row: bZbZ. For the signal, a 100% B -> bH branching fraction is assumed. The hatched regions for the background and background plus signal distributions indicate the systematic uncertainties. All three data-taking years are combined.
Data (black points), expected background (solid blue histogram), and expected background plus a VLQ signal for different VLQ masses (colored lines). Left column: 4-jet events, center column: 5-jet events, right column: 6-jet events; upper row: bHbH, middle row: bHbZ,lower row: bZbZ. For the signal, a 100% B -> bH branching fraction is assumed. The hatched regions for the background and background plus signal distributions indicate the systematic uncertainties. All three data-taking years are combined.
Data (black points), expected background (solid blue histogram), and expected background plus a VLQ signal for different VLQ masses (colored lines). Left column: 4-jet events, center column: 5-jet events, right column: 6-jet events; upper row: bHbH, middle row: bHbZ,lower row: bZbZ. For the signal, a 100% B -> bH branching fraction is assumed. The hatched regions for the background and background plus signal distributions indicate the systematic uncertainties. All three data-taking years are combined.
Data (black points), expected background (solid blue histogram), and expected background plus a VLQ signal for different VLQ masses (colored lines). Left column: 4-jet events, center column: 5-jet events, right column: 6-jet events; upper row: bHbH, middle row: bHbZ,lower row: bZbZ. For the signal, a 100% B -> bH branching fraction is assumed. The hatched regions for the background and background plus signal distributions indicate the systematic uncertainties. All three data-taking years are combined.
Data (black points), expected background (solid blue histogram), and expected background plus a VLQ signal for different VLQ masses (colored lines). Left column: 4-jet events, center column: 5-jet events, right column: 6-jet events; upper row: bHbH, middle row: bHbZ,lower row: bZbZ. For the signal, a 100% B -> bH branching fraction is assumed. The hatched regions for the background and background plus signal distributions indicate the systematic uncertainties. All three data-taking years are combined.
Data (black points), expected background (solid blue histogram), and expected background plus a VLQ signal for different VLQ masses (colored lines). Left column: 4-jet events, center column: 5-jet events, right column: 6-jet events; upper row: bHbH, middle row: bHbZ,lower row: bZbZ. For the signal, a 100% B -> bH branching fraction is assumed. The hatched regions for the background and background plus signal distributions indicate the systematic uncertainties. All three data-taking years are combined.
Data (black points), expected background (solid blue histogram), and expected background plus a VLQ signal for different VLQ masses (colored lines). Left column: 4-jet events, center column: 5-jet events, right column: 6-jet events; upper row: bHbH, middle row: bHbZ,lower row: bZbZ. For the signal, a 100% B -> bH branching fraction is assumed. The hatched regions for the background and background plus signal distributions indicate the systematic uncertainties. All three data-taking years are combined.
Expected limits on the VLQ mass at 95% CL as a function of the branching fractions $\mathcal{B}$( B -> bH ) and $\mathcal{B}$( B -> bZ)
Observed limits on the VLQ mass at 95% CL as a function of the branching fractions $\mathcal{B}$( B -> bH ) and $\mathcal{B}$( B -> bZ)
The 95% confidence limit on the cross section for VLQ pair production as a function of VLQ mass for three branching fraction hypotheses: B( B -> bH ) = 100% (upper left), B( B -> bZ ) = 100% (upper right), and and B( B -> bH ) = B( B -> bZ ) = 50% (lower). The solid black line indicates the observed limit and the dashed line indicates the expected limit with 1 sigma (green band) and 2 sigma (yellow band) uncertainties. The theoretical cross section and its uncertainty are shown as the red line and pale red band.
The 95% confidence limit on the cross section for VLQ pair production as a function of VLQ mass for three branching fraction hypotheses: B( B -> bH ) = 100% (upper left), B( B -> bZ ) = 100% (upper right), and and B( B -> bH ) = B( B -> bZ ) = 50% (lower). The solid black line indicates the observed limit and the dashed line indicates the expected limit with 1 sigma (green band) and 2 sigma (yellow band) uncertainties. The theoretical cross section and its uncertainty are shown as the red line and pale red band.
The 95% confidence limit on the cross section for VLQ pair production as a function of VLQ mass for three branching fraction hypotheses: B( B -> bH ) = 100% (upper left), B( B -> bZ ) = 100% (upper right), and and B( B -> bH ) = B( B -> bZ ) = 50% (lower). The solid black line indicates the observed limit and the dashed line indicates the expected limit with 1 sigma (green band) and 2 sigma (yellow band) uncertainties. The theoretical cross section and its uncertainty are shown as the red line and pale red band.
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