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In order to study further the long-range correlations ("ridge") observed recently in p+Pb collisions at sqrt(s_NN) =5.02 TeV, the second-order azimuthal anisotropy parameter of charged particles, v_2, has been measured with the cumulant method using the ATLAS detector at the LHC. In a data sample corresponding to an integrated luminosity of approximately 1 microb^(-1), the parameter v_2 has been obtained using two- and four-particle cumulants over the pseudorapidity range |eta|<2.5. The results are presented as a function of transverse momentum and the event activity, defined in terms of the transverse energy summed over 3.1<eta<4.9 in the direction of the Pb beam. They show features characteristic of collective anisotropic flow, similar to that observed in Pb+Pb collisions. A comparison is made to results obtained using two-particle correlation methods, and to predictions from hydrodynamic models of p+Pb collisions. Despite the small transverse spatial extent of the p+Pb collision system, the large magnitude of v_2 and its similarity to hydrodynamic predictions provide additional evidence for the importance of final-state effects in p+Pb reactions.
The second flow harmonic measured with the two-particle cumulants as a function of transverse momentum in the event activity bin of 25-40 GeV.
The second flow harmonic measured with the two-particle cumulants as a function of transverse momentum in the event activity bin of 40-55 GeV.
The second flow harmonic measured with the two-particle cumulants as a function of transverse momentum in the event activity bin of 55-80 GeV.
The second flow harmonic measured with the two-particle cumulants as a function of transverse momentum in the event activity bin of >80 GeV.
The second flow harmonic measured with the four-particle cumulants as a function of transverse momentum in the event activity bin of 25-40 GeV.
The second flow harmonic measured with the four-particle cumulants as a function of transverse momentum in the event activity bin of 40-55 GeV.
The second flow harmonic measured with the four-particle cumulants as a function of transverse momentum in the event activity bin of 55-80 GeV.
The second flow harmonic measured with the four-particle cumulants as a function of transverse momentum in the event activity bin of >80 GeV.
The second-order harmonic, v2, integrated over pT and eta, calculated with two-particle cumulants as a function of Sum ET^Pb.
The second-order harmonic, v2, integrated over pT and eta, calculated with four-particle cumulants as a function of Sum ET^Pb.
Correlations between the elliptic or triangular flow coefficients $v_m$ ($m$=2 or 3) and other flow harmonics $v_n$ ($n$=2 to 5) are measured using $\sqrt{s_{NN}}=2.76$ TeV Pb+Pb collision data collected in 2010 by the ATLAS experiment at the LHC, corresponding to an integrated lumonisity of 7 $\mu$b$^{-1}$. The $v_m$-$v_n$ correlations are measured in midrapidity as a function of centrality, and, for events within the same centrality interval, as a function of event ellipticity or triangularity defined in a forward rapidity region. For events within the same centrality interval, $v_3$ is found to be anticorrelated with $v_2$ and this anticorrelation is consistent with similar anticorrelations between the corresponding eccentricities $\epsilon_2$ and $\epsilon_3$. On the other hand, it is observed that $v_4$ increases strongly with $v_2$, and $v_5$ increases strongly with both $v_2$ and $v_3$. The trend and strength of the $v_m$-$v_n$ correlations for $n$=4 and 5 are found to disagree with $\epsilon_m$-$\epsilon_n$ correlations predicted by initial-geometry models. Instead, these correlations are found to be consistent with the combined effects of a linear contribution to $v_n$ and a nonlinear term that is a function of $v_2^2$ or of $v_2v_3$, as predicted by hydrodynamic models. A simple two-component fit is used to separate these two contributions. The extracted linear and nonlinear contributions to $v_4$ and $v_5$ are found to be consistent with previously measured event-plane correlations.
$v_{2}$ data for various $q_2$ bins, Centrality 0-5%.
$v_{3}$ data for various $q_2$ bins, Centrality 0-5%.
$v_{4}$ data for various $q_2$ bins, Centrality 0-5%.
$v_{5}$ data for various $q_2$ bins, Centrality 0-5%.
$v_{2}$ data for various $q_2$ bins, Centrality 5-10%.
$v_{3}$ data for various $q_2$ bins, Centrality 5-10%.
$v_{4}$ data for various $q_2$ bins, Centrality 5-10%.
$v_{5}$ data for various $q_2$ bins, Centrality 5-10%.
$v_{2}$ data for various $q_2$ bins, Centrality 10-15%.
$v_{3}$ data for various $q_2$ bins, Centrality 10-15%.
$v_{4}$ data for various $q_2$ bins, Centrality 10-15%.
$v_{5}$ data for various $q_2$ bins, Centrality 10-15%.
$v_{2}$ data for various $q_2$ bins, Centrality 15-20%.
$v_{3}$ data for various $q_2$ bins, Centrality 15-20%.
$v_{4}$ data for various $q_2$ bins, Centrality 15-20%.
$v_{5}$ data for various $q_2$ bins, Centrality 15-20%.
$v_{2}$ data for various $q_2$ bins, Centrality 20-25%.
$v_{3}$ data for various $q_2$ bins, Centrality 20-25%.
$v_{4}$ data for various $q_2$ bins, Centrality 20-25%.
$v_{5}$ data for various $q_2$ bins, Centrality 20-25%.
$v_{2}$ data for various $q_2$ bins, Centrality 25-30%.
$v_{3}$ data for various $q_2$ bins, Centrality 25-30%.
$v_{4}$ data for various $q_2$ bins, Centrality 25-30%.
$v_{5}$ data for various $q_2$ bins, Centrality 25-30%.
$v_{2}$ data for various $q_2$ bins, Centrality 30-35%.
$v_{3}$ data for various $q_2$ bins, Centrality 30-35%.
$v_{4}$ data for various $q_2$ bins, Centrality 30-35%.
$v_{5}$ data for various $q_2$ bins, Centrality 30-35%.
$v_{2}$ data for various $q_2$ bins, Centrality 35-40%.
$v_{3}$ data for various $q_2$ bins, Centrality 35-40%.
$v_{4}$ data for various $q_2$ bins, Centrality 35-40%.
$v_{5}$ data for various $q_2$ bins, Centrality 35-40%.
$v_{2}$ data for various $q_2$ bins, Centrality 40-45%.
$v_{3}$ data for various $q_2$ bins, Centrality 40-45%.
$v_{4}$ data for various $q_2$ bins, Centrality 40-45%.
$v_{5}$ data for various $q_2$ bins, Centrality 40-45%.
$v_{2}$ data for various $q_2$ bins, Centrality 45-50%.
$v_{3}$ data for various $q_2$ bins, Centrality 45-50%.
$v_{4}$ data for various $q_2$ bins, Centrality 45-50%.
$v_{5}$ data for various $q_2$ bins, Centrality 45-50%.
$v_{2}$ data for various $q_2$ bins, Centrality 50-55%.
$v_{3}$ data for various $q_2$ bins, Centrality 50-55%.
$v_{4}$ data for various $q_2$ bins, Centrality 50-55%.
$v_{5}$ data for various $q_2$ bins, Centrality 50-55%.
$v_{2}$ data for various $q_2$ bins, Centrality 55-60%.
$v_{3}$ data for various $q_2$ bins, Centrality 55-60%.
$v_{4}$ data for various $q_2$ bins, Centrality 55-60%.
$v_{5}$ data for various $q_2$ bins, Centrality 55-60%.
$v_{2}$ data for various $q_2$ bins, Centrality 60-65%.
$v_{3}$ data for various $q_2$ bins, Centrality 60-65%.
$v_{4}$ data for various $q_2$ bins, Centrality 60-65%.
$v_{5}$ data for various $q_2$ bins, Centrality 60-65%.
$v_{2}$ data for various $q_2$ bins, Centrality 65-70%.
$v_{3}$ data for various $q_2$ bins, Centrality 65-70%.
$v_{4}$ data for various $q_2$ bins, Centrality 65-70%.
$v_{5}$ data for various $q_2$ bins, Centrality 65-70%.
$v_{2}$ data for various $q_2$ bins, Centrality 0-10%.
$v_{3}$ data for various $q_2$ bins, Centrality 0-10%.
$v_{4}$ data for various $q_2$ bins, Centrality 0-10%.
$v_{5}$ data for various $q_2$ bins, Centrality 0-10%.
$v_{2}$ data for various $q_2$ bins, Centrality 10-20%.
$v_{3}$ data for various $q_2$ bins, Centrality 10-20%.
$v_{4}$ data for various $q_2$ bins, Centrality 10-20%.
$v_{5}$ data for various $q_2$ bins, Centrality 10-20%.
$v_{2}$ data for various $q_2$ bins, Centrality 20-30%.
$v_{3}$ data for various $q_2$ bins, Centrality 20-30%.
$v_{4}$ data for various $q_2$ bins, Centrality 20-30%.
$v_{5}$ data for various $q_2$ bins, Centrality 20-30%.
$v_{2}$ data for various $q_2$ bins, Centrality 30-40%.
$v_{3}$ data for various $q_2$ bins, Centrality 30-40%.
$v_{4}$ data for various $q_2$ bins, Centrality 30-40%.
$v_{5}$ data for various $q_2$ bins, Centrality 30-40%.
$v_{2}$ data for various $q_2$ bins, Centrality 40-50%.
$v_{3}$ data for various $q_2$ bins, Centrality 40-50%.
$v_{4}$ data for various $q_2$ bins, Centrality 40-50%.
$v_{5}$ data for various $q_2$ bins, Centrality 40-50%.
$v_{2}$ data for various $q_3$ bins, Centrality 0-5%.
$v_{3}$ data for various $q_3$ bins, Centrality 0-5%.
$v_{4}$ data for various $q_3$ bins, Centrality 0-5%.
$v_{5}$ data for various $q_3$ bins, Centrality 0-5%.
$v_{2}$ data for various $q_3$ bins, Centrality 5-10%.
$v_{3}$ data for various $q_3$ bins, Centrality 5-10%.
$v_{4}$ data for various $q_3$ bins, Centrality 5-10%.
$v_{5}$ data for various $q_3$ bins, Centrality 5-10%.
$v_{2}$ data for various $q_3$ bins, Centrality 10-15%.
$v_{3}$ data for various $q_3$ bins, Centrality 10-15%.
$v_{4}$ data for various $q_3$ bins, Centrality 10-15%.
$v_{5}$ data for various $q_3$ bins, Centrality 10-15%.
$v_{2}$ data for various $q_3$ bins, Centrality 15-20%.
$v_{3}$ data for various $q_3$ bins, Centrality 15-20%.
$v_{4}$ data for various $q_3$ bins, Centrality 15-20%.
$v_{5}$ data for various $q_3$ bins, Centrality 15-20%.
$v_{2}$ data for various $q_3$ bins, Centrality 20-25%.
$v_{3}$ data for various $q_3$ bins, Centrality 20-25%.
$v_{4}$ data for various $q_3$ bins, Centrality 20-25%.
$v_{5}$ data for various $q_3$ bins, Centrality 20-25%.
$v_{2}$ data for various $q_3$ bins, Centrality 25-30%.
$v_{3}$ data for various $q_3$ bins, Centrality 25-30%.
$v_{4}$ data for various $q_3$ bins, Centrality 25-30%.
$v_{5}$ data for various $q_3$ bins, Centrality 25-30%.
$v_{2}$ data for various $q_3$ bins, Centrality 30-35%.
$v_{3}$ data for various $q_3$ bins, Centrality 30-35%.
$v_{4}$ data for various $q_3$ bins, Centrality 30-35%.
$v_{5}$ data for various $q_3$ bins, Centrality 30-35%.
$v_{2}$ data for various $q_3$ bins, Centrality 35-40%.
$v_{3}$ data for various $q_3$ bins, Centrality 35-40%.
$v_{4}$ data for various $q_3$ bins, Centrality 35-40%.
$v_{5}$ data for various $q_3$ bins, Centrality 35-40%.
$v_{2}$ data for various $q_3$ bins, Centrality 40-45%.
$v_{3}$ data for various $q_3$ bins, Centrality 40-45%.
$v_{4}$ data for various $q_3$ bins, Centrality 40-45%.
$v_{5}$ data for various $q_3$ bins, Centrality 40-45%.
$v_{2}$ data for various $q_3$ bins, Centrality 45-50%.
$v_{3}$ data for various $q_3$ bins, Centrality 45-50%.
$v_{4}$ data for various $q_3$ bins, Centrality 45-50%.
$v_{5}$ data for various $q_3$ bins, Centrality 45-50%.
$v_{2}$ data for various $q_3$ bins, Centrality 50-55%.
$v_{3}$ data for various $q_3$ bins, Centrality 50-55%.
$v_{4}$ data for various $q_3$ bins, Centrality 50-55%.
$v_{5}$ data for various $q_3$ bins, Centrality 50-55%.
$v_{2}$ data for various $q_3$ bins, Centrality 55-60%.
$v_{3}$ data for various $q_3$ bins, Centrality 55-60%.
$v_{4}$ data for various $q_3$ bins, Centrality 55-60%.
$v_{5}$ data for various $q_3$ bins, Centrality 55-60%.
$v_{2}$ data for various $q_3$ bins, Centrality 60-65%.
$v_{3}$ data for various $q_3$ bins, Centrality 60-65%.
$v_{4}$ data for various $q_3$ bins, Centrality 60-65%.
$v_{5}$ data for various $q_3$ bins, Centrality 60-65%.
$v_{2}$ data for various $q_3$ bins, Centrality 65-70%.
$v_{3}$ data for various $q_3$ bins, Centrality 65-70%.
$v_{4}$ data for various $q_3$ bins, Centrality 65-70%.
$v_{5}$ data for various $q_3$ bins, Centrality 65-70%.
$v_{2}$ data for various $q_3$ bins, Centrality 0-10%.
$v_{3}$ data for various $q_3$ bins, Centrality 0-10%.
$v_{4}$ data for various $q_3$ bins, Centrality 0-10%.
$v_{5}$ data for various $q_3$ bins, Centrality 0-10%.
$v_{2}$ data for various $q_3$ bins, Centrality 10-20%.
$v_{3}$ data for various $q_3$ bins, Centrality 10-20%.
$v_{4}$ data for various $q_3$ bins, Centrality 10-20%.
$v_{5}$ data for various $q_3$ bins, Centrality 10-20%.
$v_{2}$ data for various $q_3$ bins, Centrality 20-30%.
$v_{3}$ data for various $q_3$ bins, Centrality 20-30%.
$v_{4}$ data for various $q_3$ bins, Centrality 20-30%.
$v_{5}$ data for various $q_3$ bins, Centrality 20-30%.
$v_{2}$ data for various $q_3$ bins, Centrality 30-40%.
$v_{3}$ data for various $q_3$ bins, Centrality 30-40%.
$v_{4}$ data for various $q_3$ bins, Centrality 30-40%.
$v_{5}$ data for various $q_3$ bins, Centrality 30-40%.
$v_{2}$ data for various $q_3$ bins, Centrality 40-50%.
$v_{3}$ data for various $q_3$ bins, Centrality 40-50%.
$v_{4}$ data for various $q_3$ bins, Centrality 40-50%.
$v_{5}$ data for various $q_3$ bins, Centrality 40-50%.
$v_{2}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{2}$ correlation within each centrality.
$v_{2}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{2}$ correlation within each centrality.
$v_{2}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{2}$ correlation within each centrality.
$v_{2}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{2}$ correlation within each centrality.
$v_{2}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{2}$ correlation within each centrality.
$v_{3}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.
$v_{3}$ - $v_{3}$ correlation within each centrality.
$v_{3}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.
$v_{3}$ - $v_{3}$ correlation within each centrality.
$v_{3}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.
$v_{3}$ - $v_{3}$ correlation within each centrality.
$v_{3}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.
$v_{3}$ - $v_{3}$ correlation within each centrality.
$v_{2}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{3}$ correlation for various q2 bins within each centrality.
$v_{2}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{3}$ correlation for various q2 bins within each centrality.
$v_{2}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{3}$ correlation for various q2 bins within each centrality.
$v_{2}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{3}$ correlation for various q2 bins within each centrality.
linear fit result of $v_{2}$ - $v_{3}$ correlation within each centrality.
$v_{3}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.
$v_{3}$ - $v_{2}$ correlation for various q3 bins within each centrality.
$v_{3}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.
$v_{3}$ - $v_{2}$ correlation for various q3 bins within each centrality.
$v_{3}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.
$v_{3}$ - $v_{2}$ correlation for various q3 bins within each centrality.
$v_{3}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.
$v_{3}$ - $v_{2}$ correlation for various q3 bins within each centrality.
$v_{2}$ - $v_{4}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{4}$ correlation for various q2 bins within each centrality.
$v_{2}$ - $v_{4}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{4}$ correlation for various q2 bins within each centrality.
$v_{2}$ - $v_{4}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{4}$ correlation for various q2 bins within each centrality.
$v_{2}$ - $v_{4}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{4}$ correlation for various q2 bins within each centrality.
$v_{3}$ - $v_{4}$ inclusive correlation in 5% centrality intervals.
$v_{3}$ - $v_{4}$ correlation within each centrality.
$v_{3}$ - $v_{4}$ inclusive correlation in 5% centrality intervals.
$v_{3}$ - $v_{4}$ correlation within each centrality.
$v_4$ decomposed into linear and nonlinear contributions based on q2 event-shape selection.
$v_4$ decomposed into linear and nonlinear contributions based on q2 event-shape selection.
$v_4$ decomposed into linear and nonlinear contributions based on q2 event-shape selection.
$v_4$ decomposed into linear and nonlinear contributions based on q2 event-shape selection.
$v_4$ decomposed into linear and nonlinear contributions based on q2 event-shape selection.
$v_5$ decomposed into linear and nonlinear contributions based on q2 event-shape selection.
$v_5$ decomposed into linear and nonlinear contributions based on q3 event-shape selection.
RMS eccentricity scaled v_n.
RMS eccentricity scaled v_n.
$v_{2}$ - $v_{5}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{5}$ correlation for various q2 bins within each centrality.
$v_{3}$ - $v_{5}$ inclusive correlation in 5% centrality intervals.
$v_{3}$ - $v_{5}$ correlation for various q2 bins within each centrality.
An inclusive search for long-lived exotic particles (LLPs) decaying to final states with a pair of muons is presented. The search uses data corresponding to an integrated luminosity of 36.6 fb$^{-1}$ collected by the CMS experiment from the proton-proton collisions at $\sqrt{s}$ = 13.6 TeV in 2022, the first year of Run 3 of the CERN LHC. The experimental signature is a pair of oppositely charged muons originating from a common vertex spatially separated from the proton-proton interaction point by distances ranging from several hundred $\mu$m to several meters. The sensitivity of the search benefits from new triggers for displaced dimuons developed for Run 3. The results are interpreted in the framework of the hidden Abelian Higgs model, in which the Higgs boson decays to a pair of long-lived dark photons, and of an $R$-parity violating supersymmetry model, in which long-lived neutralinos decay to a pair of muons and a neutrino. The limits set on these models are the most stringent to date in wide regions of lifetimes for LLPs with masses larger than 10 GeV.
Efficiencies of the various displaced dimuon trigger paths and their combination as a function of $c\tau$ for the HAHM signal events with $m(Z_D) = 20\ GeV$. The efficiency is defined as the fraction of simulated events that satisfy the detector acceptance and the requirements of the following sets of trigger paths: the Run 2 (2018) triggers (dashed black); the Run 3 (2022, L3) triggers (blue); the Run 3 (2022, L2) triggers (red); and the OR of all these triggers (Run 3 (2022), black). The lower panel shows the ratio of the overall Run 3 (2022) efficiency to the Run 2 (2018) efficiency.
Overall efficiencies in the STA-STA (green) and TMS-TMS (red) dimuon categories, as well as their combination (black) as a function of $c\tau$ for the HAHM signal events with $m(Z_D) = 20\ GeV$. The solid curves show efficiencies achieved with the 2022 Run 3 triggers, whereas dashed curves show efficiencies for the subset of events selected by the triggers used in the 2018 Run 2 analysis. The efficiency is defined as the fraction of signal events that satisfy the criteria of the indicated trigger as well as the full set of offline selection criteria. The lower panel shows the relative improvement of the overall signal efficiency brought in by improvements in the trigger.
Comparison of the observed (black points) and expected (histograms) numbers of events in nonoverlapping $m_{\mu \mu}$ intervals in the STA-STA dimuon category, in the signal region optimized for the HAHM model. Yellow and green stacked filled histograms represent mean expected background contributions from QCD and DY, respectively, while statistical uncertainties in the total expected background are shown as hatched histograms. Signal contributions expected from simulated signals indicated in the legends are shown in red and blue. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.
Comparison of the observed (black points) and expected (histograms) numbers of events in nonoverlapping $m^{corr}_{\mu\mu}$ intervals in the STA-STA dimuon category, in the signal region optimized for the RPV SUSY model. Yellow and green stacked filled histograms represent mean expected background contributions from QCD and DY, respectively, while statistical uncertainties in the total expected background are shown as hatched histograms. Signal contributions expected from simulated signals indicated in the legends are shown in red and blue. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.
Distribution of min($d_0 / \sigma_{d_0}$) for TMS-TMS dimuons with $|\Delta\Phi| < \pi/30$, for events in all mass intervals combined, for both the validation (min($d_0 / \sigma_{d_0}$) < 6) and signal (min($d_0 / \sigma_{d_0}$) > 6) regions. The number of observed events (black circles) is overlaid with the stacked histograms showing the expected numbers of QCD (yellow) and DY (green) background events. Statistical uncertainties in the total expected background are shown as hatched histograms. Signal contributions expected from simulated signals indicated in the legends are shown in red and blue. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. Events are required to satisfy all nominal selection criteria with the exception of the $d_0 / \sigma_{d_0}$ requirement. The last bin includes events in the histogram overflow.
Distribution of min($d_0 / \sigma_{d_0}$) for TMS-TMS dimuons with $|\Delta\Phi| < \pi/4$, for events in all mass intervals combined, for both the validation (min($d_0 / \sigma_{d_0}$) < 6) and signal (min($d_0 / \sigma_{d_0}$) > 6) regions. The number of observed events (black circles) is overlaid with the stacked histograms showing the expected numbers of QCD (yellow) and DY (green) background events. Statistical uncertainties in the total expected background are shown as hatched histograms. Signal contributions expected from simulated signals indicated in the legends are shown in red and blue. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. Events are required to satisfy all nominal selection criteria with the exception of the $d_0 / \sigma_{d_0}$ requirement. The last bin includes events in the histogram overflow.
Comparison of observed and expected numbers of events in bins of $m^{corr}_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the RPV SUSY model. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m^{corr}_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 6-10. Hatched histograms show statistical uncertainties in the total expected background. Contributions expected from signal events predicted by the RPV SUSY model with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.
Comparison of observed and expected numbers of events in bins of $m^{corr}_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the RPV SUSY model. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m^{corr}_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 10-20. Hatched histograms show statistical uncertainties in the total expected background. Contributions expected from signal events predicted by the RPV SUSY model with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.
Comparison of observed and expected numbers of events in bins of $m^{corr}_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the RPV SUSY model. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m^{corr}_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: >20. Hatched histograms show statistical uncertainties in the total expected background. Contributions expected from signal events predicted by the RPV SUSY model with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.
Comparison of observed and expected numbers of events in bins of $m_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the HAHM. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 6-10. Hatched histograms show statistical uncertainties in the total expected background. Signal contributions expected from simulated $H \rightarrow Z_DZ_D$ events with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.
Comparison of observed and expected numbers of events in bins of $m_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the HAHM. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 10-20. Hatched histograms show statistical uncertainties in the total expected background. Signal contributions expected from simulated $H \rightarrow Z_DZ_D$ events with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.
Comparison of observed and expected numbers of events in bins of $m_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the HAHM. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: >20. Hatched histograms show statistical uncertainties in the total expected background. Signal contributions expected from simulated $H \rightarrow Z_DZ_D$ events with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 10\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 20\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 30\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 40\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 50\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 60\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 10\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 20\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 30\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 40\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 50\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 60\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 125\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The predicted cross section for $m(\tilde{q}) = 125\ GeV$ is 7200 pb, and falls outside the y-axis range.
The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 200\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The predicted cross section for $m(\tilde{q}) = 200 GeV$ is 840 pb, and falls outside the y-axis range.
The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 350\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The predicted cross section for $m(\tilde{q}) = 350\ GeV$ is 50 pb, and falls outside the y-axis range.
The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 700\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The gray horizontal line indicates the theoretical value of the squark-antisquark production cross section with the uncertainties shown as the gray shaded band.
The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 1150\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The gray horizontal line indicates the theoretical value of the squark-antisquark production cross section with the uncertainties shown as the gray shaded band.
The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 1600\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The gray horizontal line indicates the theoretical value of the squark-antisquark production cross section with the uncertainties shown as the gray shaded band.
Fractions of signal events with zero (green), one (blue), and two (red) STA muons matched to TMS muons by the STA to TMS association procedure, as a function of generated $L_{xy}$, in all HAHM signal samples combined.
Efficiencies of the Run 2 and Run 3 displaced dimuon triggers as a function of $c\tau$ for the HAHM signal events with $m(Z_D) = 50\ GeV$. The efficiency is defined as the fraction of simulated events that satisfy the requirements of the following sets of trigger paths: the Run 2 (2018) triggers (dashed black); the Run 3 (2022, L3) triggers (blue); the Run 3 (2022, L2) triggers (red); and the OR of all these triggers (Run 3 (2022), black). The lower panel shows the ratio of the overall Run 3 (2022) efficiency to the Run 2 (2018) efficiency.
Efficiencies of the Run 2 (2018) (red) and Run 3 (2022) (black) sets of displaced dimuon triggers as a function of $m(Z_D)$ for the HAHM signal events with $c\tau = 1\ cm$. The efficiency is defined as the fraction of simulated events that satisfy the detector acceptance and the requirements of the indicated set of trigger paths. The lower panel shows the ratio of the Run 3 (2022) efficiency to the Run 2 (2018) efficiency.
Efficiencies of the Run 2 (2018) (red) and Run 3 (2022) (black) sets of displaced dimuon triggers as a function of $m(Z_D)$ for the HAHM signal events with $c\tau = 10\ m$. The efficiency is defined as the fraction of simulated events that satisfy the detector acceptance and the requirements of the indicated set of trigger paths. The lower panel shows the ratio of the Run 3 (2022) efficiency to the Run 2 (2018) efficiency.
Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM signal with $m(Z_D) = 20\ GeV$ in different years of data taking. Efficiencies are computed as the ratios of the number of simulated signal events in which at least one dimuon candidate passes all 2016 (dashed green), 2018 (dashed red), and 2022 (solid black) trigger and offline selection criteria to the total number of simulated signal events. The lower panel shows the ratio of the 2022 efficiency to the 2018 efficiency (dashed red) and to the 2016 efficiency (dashed green).
Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM signal with $m(Z_D) = 50\ GeV$ in different years of data taking. Efficiencies are computed as the ratios of the number of simulated signal events in which at least one dimuon candidate passes all 2016 (dashed green), 2018 (dashed red), and 2022 (solid black) trigger and offline selection criteria to the total number of simulated signal events. The lower panel shows the ratio of the 2022 efficiency to the 2018 efficiency (dashed red) and to the 2016 efficiency (dashed green).
Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 10\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 20\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 30\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 40\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 60\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 125\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 200\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 350\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 700\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 1150\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 1600\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 700\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 500\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 1150\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 500\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 1600\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 500\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies in the TMS-TMS dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ smaller than 20 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ smaller than 20 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies in the TMS-TMS dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 20-70 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 20-70 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 70-500 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies in the TMS-TMS dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ smaller than 20 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ smaller than 20 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies in the TMS-TMS dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 20-70 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 20-70 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 70-500 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
A search for the lepton flavor violating $\tau$$\to$ 3$\mu$ decay is performed using proton-proton collision events at a center-of-mass energy of 13 TeV collected by the CMS experiment at the LHC in 2017-2018, corresponding to an integrated luminosity of 97.7 fb$^{-1}$. Tau leptons produced in both heavy-flavor hadron and W boson decays are exploited in the analysis. No evidence for the decay is observed. The results of this search are combined with an earlier null result based on data collected in 2016 to obtain a total integrated luminosity of 131 fb$^{-1}$. The observed (expected) upper limits on the branching fraction $\mathcal{B}$($\tau$$\to$ 3$\mu$) at confidence levels of 90 and 95% are 2.9 $\times$ 10$^{-8}$ (2.4 $\times$ 10$^{-8}$) and 3.6 $\times$ 10$^{-8}$ (3.0 $\times$ 10$^{-8}$), respectively.
Expected and observed upper limits on the $\tau\to3\mu$ branching fraction at 90% of confidence level for different categories of the analyis.
Expected and observed upper limits on the $\tau\to3\mu$ branching fraction at 95% of confidence level for the Run2 combination.
A generic search is presented for the associated production of a Z boson or a photon with an additional unspecified massive particle X, pp $\to$ pp + Z/$\gamma$ + X, in proton-tagged events from proton-proton collisions at $\sqrt{s}$ = 13 TeV, recorded in 2017 with the CMS detector and the CMS-TOTEM precision proton spectrometer. The missing mass spectrum is analysed in the 600-1600 GeV range and a fit is performed to search for possible deviations from the background expectation. No significant excess in data with respect to the background predictions has been observed. Model-independent upper limits on the visible production cross section of pp $\to$ pp + Z/$\gamma$ + X are set.
Comparison of the $m_{miss}$ shapes for the simulated signal events within the fiducial region and those outside it, after including the effect of PU protons as describe in the text, for a generated $m_{X}$ mass of 1000 GeV. The distributions are shown for multi(+z)-multi(−z) proton reconstruction categories.
Comparison of the $m_{miss}$ shapes for the simulated signal events within the fiducial region and those outside it, after including the effect of PU protons as describe in the text, for a generated $m_{X}$ mass of 1000 GeV. The distributions are shown for multi(+z)-single(−z) proton reconstruction categories.
Comparison of the $m_{miss}$ shapes for the simulated signal events within the fiducial region and those outside it, after including the effect of PU protons as describe in the text, for a generated $m_{X}$ mass of 1000 GeV. The distributions are shown for single(+z)-multi(−z) proton reconstruction categories.
Comparison of the $m_{miss}$ shapes for the simulated signal events within the fiducial region and those outside it, after including the effect of PU protons as describe in the text, for a generated $m_{X}$ mass of 1000 GeV. The distributions are shown for single(+z)-single(−z) proton reconstruction categories.
Product of the acceptance times the combined reconstruction and identification efficiency, as a function of $m_{X}$ , for events generated inside the fiducial volume defined in Table 1. The curves shown panel display the different final states. The definition of the fiducial region and signal model used to estimate the acceptance is provided in the text.
Product of the acceptance times the combined reconstruction and identification efficiency, as a function of $m_{X}$ , for different proton reconstruction categories in the $Z \rightarrow \mu\mu $ analysis, and for events generated inside the fiducial volume defined in Table 1. The curves shown panel display the different final states. The definition of the fiducial region and signal model used to estimate the acceptance is provided in the text.
Distributions of the reconstructed proton $\xi$ in the negative arm from the proton mixing procedure with simulated MC events, are compared to data. The ee final states are shown.The ee events are displayed without the Z boson $p_T$ requirement.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.
Distributions of the reconstructed proton $\xi$ in the positive arm from the proton mixing procedure with simulated MC events, are compared to data. The ee final states are shown.The ee events are displayed without the Z boson $p_T$ requirement.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.
Distributions of the reconstructed di-proton rapidity from the proton mixing procedure with simulated MC events, are compared to data. The ee final states are shown.The ee events are displayed without the Z boson $p_T$ requirement.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.
Distributions of the reconstructed proton $\xi$ in the negative arm from the proton mixing procedure with simulated MC events, are compared to data. The $\mu\mu$ final states are shown.The $\mu\mu$ events are displayed without the Z boson $p_T$ requirement.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.
Distributions of the reconstructed proton $\xi$ in the positive arm from the proton mixing procedure with simulated MC events, are compared to data. The $\mu\mu$ final states are shown.The $\mu\mu$ events are displayed without the Z boson $p_T$ requirement.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.
Distributions of the reconstructed di-proton rapidity from the proton mixing procedure with simulated MC events, are compared to data. The $\mu\mu$ final states are shown.The $\mu\mu$ events are displayed without the Z boson $p_T$ requirement.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.
Distributions of the reconstructed proton $\xi$ in the negative arm from the proton mixing procedure with simulated MC events, are compared to data. The $\gamma$ final states are shown.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.
Distributions of the reconstructed proton $\xi$ in the positive arm from the proton mixing procedure with simulated MC events, are compared to data. The $\gamma$ final states are shown.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.
Distributions of the reconstructed di-proton rapidity from the proton mixing procedure with simulated MC events, are compared to data. The $\gamma$ final states are shown.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.
Validation of the background modelling method, using the $e\mu$ control sample. Selected $e\mu$ events are mixed with protons from $Z\rightarrow \mu \mu$ events with $p_{T}(Z)<10$ GeV to simulate the combinatorial background shape, while the data points are unaltered $e\mu$ events. The proton $\xi$ distribution for the positive CT-PPS arm is shown.
Validation of the background modelling method, using the $e\mu$ control sample. Selected $e\mu$ events are mixed with protons from $Z\rightarrow \mu \mu$ events with $p_{T}(Z)<10$ GeV to simulate the combinatorial background shape, while the data points are unaltered $e\mu$ events. The proton $\xi$ distribution for the negative CT-PPS arm is shown.
Validation of the background modelling method, using the $e\mu$ control sample. Selected $e\mu$ events are mixed with protons from $Z\rightarrow \mu \mu$ events with $p_{T}(Z)<10$ GeV to simulate the combinatorial background shape, while the data points are unaltered $e\mu$ events. The di-proton invariant mass is shown.
Validation of the background modelling method, using the $e\mu$ control sample. Selected $e\mu$ events are mixed with protons from $Z\rightarrow \mu \mu$ events with $p_{T}(Z)<10$ GeV to simulate the combinatorial background shape, while the data points are unaltered $e\mu$ events. The $m_{miss}$ is shown.
$m_{miss}$ distributions in the $pp\rightarrow ppZeeX$, with the protons reconstructed with the multi(+z)-multi(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.
$m_{miss}$ distributions in the $pp\rightarrow ppZeeX$, with the protons reconstructed with the multi(+z)-single(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.
$m_{miss}$ distributions in the $pp\rightarrow ppZeeX$, with the protons reconstructed with the single(+z)-multi(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.
$m_{miss}$ distributions in the $pp\rightarrow ppZeeX$, with the protons reconstructed with the single(+z)-single(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.
$m_{miss}$ distributions in the $pp\rightarrow ppZ\mu\mu X$, with the protons reconstructed with the multi(+z)-multi(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.
$m_{miss}$ distributions in the $pp\rightarrow ppZ\mu\mu X$, with the protons reconstructed with the multi(+z)-single(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.
$m_{miss}$ distributions in the $pp\rightarrow ppZ\mu\mu X$, with the protons reconstructed with the single(+z)-multi(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.
$m_{miss}$ distributions in the $pp\rightarrow ppZ\mu\mu X$, with the protons reconstructed with the single(+z)-single(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.
$m_{miss}$ distributions in the $pp\rightarrow pp\gamma X$, with the protons reconstructed with the multi(+z)-multi(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 10 pb.
$m_{miss}$ distributions in the $pp\rightarrow pp\gamma X$, with the protons reconstructed with the multi(+z)-single(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 10 pb.
$m_{miss}$ distributions in the $pp\rightarrow pp\gamma X$, with the protons reconstructed with the single(+z)-multi(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 10 pb.
$m_{miss}$ distributions in the $pp\rightarrow pp\gamma X$, with the protons reconstructed with the single(+z)-single(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 10 pb.
Upper limits on the $pp\rightarrow ppZ/\gamma X$ cross section at 95% CL, as a function of $m_X$. The 68 and 95% central intervals of the expected limits are represented by the green and yellow bands, respectively, while the observed limit is superimposed as a curve. The plot corresponds to the $Z\rightarrow ee$ analysis.
Upper limits on the $pp\rightarrow ppZ/\gamma X$ cross section at 95% CL, as a function of $m_X$. The 68 and 95% central intervals of the expected limits are represented by the green and yellow bands, respectively, while the observed limit is superimposed as a curve. The plot corresponds to the $Z\rightarrow \mu\mu$ analysis.
Upper limits on the $pp\rightarrow ppZ/\gamma X$ cross section at 95% CL, as a function of $m_X$. The 68 and 95% central intervals of the expected limits are represented by the green and yellow bands, respectively, while the observed limit is superimposed as a curve. The plot corresponds to the $Z$ analysis.
Upper limits on the $pp\rightarrow ppZ/\gamma X$ cross section at 95% CL, as a function of $m_X$. The 68 and 95% central intervals of the expected limits are represented by the green and yellow bands, respectively, while the observed limit is superimposed as a curve. The plot corresponds to the $\gamma$ analysis.
A search is performed for charged-lepton flavor violating processes in top quark (t) production and decay. The data were collected by the CMS experiment from proton-proton collisions at a center-of-mass energy of 13 TeV and correspond to an integrated luminosity of 138 fb$^{-1}$. The selected events are required to contain one opposite-sign electron-muon pair, a third charged lepton (electron or muon), and at least one jet of which no more than one is associated with a bottom quark. Boosted decision trees are used to distinguish signal from background, exploiting differences in the kinematics of the final states particles. The data are consistent with the standard model expectation. Upper limits at 95% confidence level are placed in the context of effective field theory on the Wilson coefficients, which range between 0.024-0.424 TeV$^{-2}$ depending on the flavor of the associated light quark and the Lorentz structure of the interaction. These limits are converted to upper limits on branching fractions involving up (charm) quarks, t$\to$e$\mu$u (t$\to$e$\mu$c), of 0.032 (0.498)$\times$10$^{-6}$, 0.022 (0.369)$\times$10$^{-6}$, and 0.012 (0.216)$\times$10$^{-6}$ for tensor-like, vector-like, and scalar-like interactions, respectively.
The expected and observed upper limits on CLFV Wilson coefficients. The Limits on the Wilson coefficients are extracted from the upper limits on the cross sections.
The expected and observed upper limits on top quark CLFV branching fractions. The Limits on the top quark CLFV branching fractions are extracted from the upper limits on the Wilson coefficients.
Differential and double-differential cross sections for the production of top quark pairs in proton-proton collisions at $\sqrt{s} =$ 13 TeV are measured as a function of kinematic variables of the top quarks and the top quark-antiquark ($\mathrm{t}\overline{\mathrm{t}}$) system. In addition, kinematic variables and multiplicities of jets associated with the $\mathrm{t}\overline{\mathrm{t}}$ production are measured. This analysis is based on data collected by the CMS experiment at the LHC in 2016 corresponding to an integrated luminosity of 35.8 fb$^{-1}$. The measurements are performed in the lepton+jets decay channels with a single muon or electron and jets in the final state. The differential cross sections are presented at the particle level, within a phase space close to the experimental acceptance, and at the parton level in the full phase space. The results are compared to several standard model predictions that use different methods and approximations. The kinematic variables of the top quarks and the $\mathrm{t}\overline{\mathrm{t}}$ system are reasonably described in general, though none predict all the measured distributions. In particular, the transverse momentum distribution of the top quarks is more steeply falling than predicted. The kinematic distributions and multiplicities of jets are adequately modeled by certain combinations of next-to-leading-order calculations and parton shower models.
Absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$.
Covariance matrix of absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$.
Covariance matrix of absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$.
Absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$.
Covariance matrix of absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$.
Covariance matrix of absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$.
Absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{l})$.
Absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{l})$.
Covariance matrix of absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{l})$.
Covariance matrix of absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{l})$.
Absolute cross section at particle level as a function of $|y(\text{t}_\text{l})|$.
Absolute cross section at particle level as a function of $|y(\text{t}_\text{l})|$.
Covariance matrix of absolute cross section at particle level as a function of $|y(\text{t}_\text{l})|$.
Covariance matrix of absolute cross section at particle level as a function of $|y(\text{t}_\text{l})|$.
Absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$.
Covariance matrix of absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$.
Covariance matrix of absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.
Covariance matrix of absolute cross section at particle level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.
Covariance matrix of absolute cross section at particle level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of $|y(\text{t}\bar{\text{t}})|$.
Absolute cross section at particle level as a function of $|y(\text{t}\bar{\text{t}})|$.
Covariance matrix of absolute cross section at particle level as a function of $|y(\text{t}\bar{\text{t}})|$.
Covariance matrix of absolute cross section at particle level as a function of $|y(\text{t}\bar{\text{t}})|$.
Absolute cross section at particle level as a function of Additional jets.
Absolute cross section at particle level as a function of Additional jets.
Covariance matrix of absolute cross section at particle level as a function of Additional jets.
Covariance matrix of absolute cross section at particle level as a function of Additional jets.
Absolute cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.
Covariance matrix of absolute cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.
Covariance matrix of absolute cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.
Covariance matrix of absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.
Covariance matrix of absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.
Covariance matrix of absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.
Covariance matrix of absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Covariance matrix of absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Covariance matrix of absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Covariance matrix of absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Covariance matrix of absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Covariance matrix of absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Covariance matrix of absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Absolute cross section at particle level as a function of $p_\text{T}(b_\text{l})$.
Absolute cross section at particle level as a function of $p_\text{T}(b_\text{l})$.
Absolute cross section at particle level as a function of $p_\text{T}(b_\text{h})$.
Absolute cross section at particle level as a function of $p_\text{T}(b_\text{h})$.
Absolute cross section at particle level as a function of $p_\text{T}(j_\text{W1})$.
Absolute cross section at particle level as a function of $p_\text{T}(j_\text{W1})$.
Absolute cross section at particle level as a function of $p_\text{T}(j_\text{W2})$.
Absolute cross section at particle level as a function of $p_\text{T}(j_\text{W2})$.
Absolute cross section at particle level as a function of $p_\text{T}(j_\text{1})$.
Absolute cross section at particle level as a function of $p_\text{T}(j_\text{1})$.
Absolute cross section at particle level as a function of $p_\text{T}(j_\text{2})$.
Absolute cross section at particle level as a function of $p_\text{T}(j_\text{2})$.
Absolute cross section at particle level as a function of $p_\text{T}(j_\text{3})$.
Absolute cross section at particle level as a function of $p_\text{T}(j_\text{3})$.
Absolute cross section at particle level as a function of $p_\text{T}(j_\text{4})$.
Absolute cross section at particle level as a function of $p_\text{T}(j_\text{4})$.
Covariance matrix of absolute cross section at particle level as a function of Jet type vs. $p_\text{T}(\mathrm{jet})$.
Covariance matrix of absolute cross section at particle level as a function of Jet type vs. $p_\text{T}(\mathrm{jet})$.
Absolute cross section at particle level as a function of $|\eta(b_\text{l})|$.
Absolute cross section at particle level as a function of $|\eta(b_\text{l})|$.
Absolute cross section at particle level as a function of $|\eta(b_\text{h})|$.
Absolute cross section at particle level as a function of $|\eta(b_\text{h})|$.
Absolute cross section at particle level as a function of $|\eta(j_\text{W1})|$.
Absolute cross section at particle level as a function of $|\eta(j_\text{W1})|$.
Absolute cross section at particle level as a function of $|\eta(j_\text{W2})|$.
Absolute cross section at particle level as a function of $|\eta(j_\text{W2})|$.
Absolute cross section at particle level as a function of $|\eta(j_\text{1})|$.
Absolute cross section at particle level as a function of $|\eta(j_\text{1})|$.
Absolute cross section at particle level as a function of $|\eta(j_\text{2})|$.
Absolute cross section at particle level as a function of $|\eta(j_\text{2})|$.
Absolute cross section at particle level as a function of $|\eta(j_\text{3})|$.
Absolute cross section at particle level as a function of $|\eta(j_\text{3})|$.
Absolute cross section at particle level as a function of $|\eta(j_\text{4})|$.
Absolute cross section at particle level as a function of $|\eta(j_\text{4})|$.
Covariance matrix of absolute cross section at particle level as a function of Jet type vs. $|\eta(\text{jet})|$.
Covariance matrix of absolute cross section at particle level as a function of Jet type vs. $|\eta(\text{jet})|$.
Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(b_\text{l})$.
Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(b_\text{l})$.
Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(b_\text{h})$.
Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(b_\text{h})$.
Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{W1})$.
Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{W1})$.
Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{W2})$.
Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{W2})$.
Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{1})$.
Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{1})$.
Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{2})$.
Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{2})$.
Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{3})$.
Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{3})$.
Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{4})$.
Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{4})$.
Covariance matrix of absolute cross section at particle level as a function of Jet type vs. $\Delta R_{\text{j}_\text{t}}$.
Covariance matrix of absolute cross section at particle level as a function of Jet type vs. $\Delta R_{\text{j}_\text{t}}$.
Absolute cross section at particle level as a function of $\Delta R_\text{t}(b_\text{l})$.
Absolute cross section at particle level as a function of $\Delta R_\text{t}(b_\text{l})$.
Absolute cross section at particle level as a function of $\Delta R_\text{t}(b_\text{h})$.
Absolute cross section at particle level as a function of $\Delta R_\text{t}(b_\text{h})$.
Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{W1})$.
Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{W1})$.
Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{W2})$.
Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{W2})$.
Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{1})$.
Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{1})$.
Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{2})$.
Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{2})$.
Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{3})$.
Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{3})$.
Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{4})$.
Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{4})$.
Covariance matrix of absolute cross section at particle level as a function of Jet type vs. $\Delta R_\text{t}$.
Covariance matrix of absolute cross section at particle level as a function of Jet type vs. $\Delta R_\text{t}$.
Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$.
Covariance matrix of normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$.
Covariance matrix of normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$.
Normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$.
Covariance matrix of normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$.
Covariance matrix of normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$.
Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{l})$.
Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{l})$.
Covariance matrix of normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{l})$.
Covariance matrix of normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{l})$.
Normalized cross section at particle level as a function of $|y(\text{t}_\text{l})|$.
Normalized cross section at particle level as a function of $|y(\text{t}_\text{l})|$.
Covariance matrix of normalized cross section at particle level as a function of $|y(\text{t}_\text{l})|$.
Covariance matrix of normalized cross section at particle level as a function of $|y(\text{t}_\text{l})|$.
Normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$.
Covariance matrix of normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$.
Covariance matrix of normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.
Covariance matrix of normalized cross section at particle level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.
Covariance matrix of normalized cross section at particle level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of $|y(\text{t}\bar{\text{t}})|$.
Normalized cross section at particle level as a function of $|y(\text{t}\bar{\text{t}})|$.
Covariance matrix of normalized cross section at particle level as a function of $|y(\text{t}\bar{\text{t}})|$.
Covariance matrix of normalized cross section at particle level as a function of $|y(\text{t}\bar{\text{t}})|$.
Normalized cross section at particle level as a function of Additional jets.
Normalized cross section at particle level as a function of Additional jets.
Covariance matrix of normalized cross section at particle level as a function of Additional jets.
Covariance matrix of normalized cross section at particle level as a function of Additional jets.
Normalized cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.
Covariance matrix of normalized cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.
Covariance matrix of normalized cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.
Covariance matrix of normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.
Covariance matrix of normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.
Covariance matrix of normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.
Covariance matrix of normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Covariance matrix of normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Covariance matrix of normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Covariance matrix of normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Covariance matrix of normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Covariance matrix of normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Covariance matrix of normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at particle level as a function of $p_\text{T}(b_\text{l})$.
Normalized cross section at particle level as a function of $p_\text{T}(b_\text{l})$.
Normalized cross section at particle level as a function of $p_\text{T}(b_\text{h})$.
Normalized cross section at particle level as a function of $p_\text{T}(b_\text{h})$.
Normalized cross section at particle level as a function of $p_\text{T}(j_\text{W1})$.
Normalized cross section at particle level as a function of $p_\text{T}(j_\text{W1})$.
Normalized cross section at particle level as a function of $p_\text{T}(j_\text{W2})$.
Normalized cross section at particle level as a function of $p_\text{T}(j_\text{W2})$.
Normalized cross section at particle level as a function of $p_\text{T}(j_\text{1})$.
Normalized cross section at particle level as a function of $p_\text{T}(j_\text{1})$.
Normalized cross section at particle level as a function of $p_\text{T}(j_\text{2})$.
Normalized cross section at particle level as a function of $p_\text{T}(j_\text{2})$.
Normalized cross section at particle level as a function of $p_\text{T}(j_\text{3})$.
Normalized cross section at particle level as a function of $p_\text{T}(j_\text{3})$.
Normalized cross section at particle level as a function of $p_\text{T}(j_\text{4})$.
Normalized cross section at particle level as a function of $p_\text{T}(j_\text{4})$.
Covariance matrix of normalized cross section at particle level as a function of Jet type vs. $p_\text{T}(\mathrm{jet})$.
Covariance matrix of normalized cross section at particle level as a function of Jet type vs. $p_\text{T}(\mathrm{jet})$.
Normalized cross section at particle level as a function of $|\eta(b_\text{l})|$.
Normalized cross section at particle level as a function of $|\eta(b_\text{l})|$.
Normalized cross section at particle level as a function of $|\eta(b_\text{h})|$.
Normalized cross section at particle level as a function of $|\eta(b_\text{h})|$.
Normalized cross section at particle level as a function of $|\eta(j_\text{W1})|$.
Normalized cross section at particle level as a function of $|\eta(j_\text{W1})|$.
Normalized cross section at particle level as a function of $|\eta(j_\text{W2})|$.
Normalized cross section at particle level as a function of $|\eta(j_\text{W2})|$.
Normalized cross section at particle level as a function of $|\eta(j_\text{1})|$.
Normalized cross section at particle level as a function of $|\eta(j_\text{1})|$.
Normalized cross section at particle level as a function of $|\eta(j_\text{2})|$.
Normalized cross section at particle level as a function of $|\eta(j_\text{2})|$.
Normalized cross section at particle level as a function of $|\eta(j_\text{3})|$.
Normalized cross section at particle level as a function of $|\eta(j_\text{3})|$.
Normalized cross section at particle level as a function of $|\eta(j_\text{4})|$.
Normalized cross section at particle level as a function of $|\eta(j_\text{4})|$.
Covariance matrix of normalized cross section at particle level as a function of Jet type vs. $|\eta(\text{jet})|$.
Covariance matrix of normalized cross section at particle level as a function of Jet type vs. $|\eta(\text{jet})|$.
Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(b_\text{l})$.
Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(b_\text{l})$.
Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(b_\text{h})$.
Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(b_\text{h})$.
Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{W1})$.
Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{W1})$.
Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{W2})$.
Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{W2})$.
Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{1})$.
Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{1})$.
Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{2})$.
Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{2})$.
Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{3})$.
Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{3})$.
Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{4})$.
Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{4})$.
Covariance matrix of normalized cross section at particle level as a function of Jet type vs. $\Delta R_{\text{j}_\text{t}}$.
Covariance matrix of normalized cross section at particle level as a function of Jet type vs. $\Delta R_{\text{j}_\text{t}}$.
Normalized cross section at particle level as a function of $\Delta R_\text{t}(b_\text{l})$.
Normalized cross section at particle level as a function of $\Delta R_\text{t}(b_\text{l})$.
Normalized cross section at particle level as a function of $\Delta R_\text{t}(b_\text{h})$.
Normalized cross section at particle level as a function of $\Delta R_\text{t}(b_\text{h})$.
Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{W1})$.
Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{W1})$.
Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{W2})$.
Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{W2})$.
Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{1})$.
Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{1})$.
Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{2})$.
Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{2})$.
Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{3})$.
Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{3})$.
Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{4})$.
Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{4})$.
Covariance matrix of normalized cross section at particle level as a function of Jet type vs. $\Delta R_\text{t}$.
Covariance matrix of normalized cross section at particle level as a function of Jet type vs. $\Delta R_\text{t}$.
gap fraction at particle level.
gap fraction at particle level.
Covariance matrix of gap fraction at particle level.
Covariance matrix of gap fraction at particle level.
gap fraction at particle level.
gap fraction at particle level.
Covariance matrix of gap fraction at particle level.
Covariance matrix of gap fraction at particle level.
jet multiplicities for $p_{T}(jet) > 30.0$ GeV.
jet multiplicities for $p_{T}(jet) > 30.0$ GeV.
jet multiplicities for $p_{T}(jet) > 50.0$ GeV.
jet multiplicities for $p_{T}(jet) > 50.0$ GeV.
jet multiplicities for $p_{T}(jet) > 75.0$ GeV.
jet multiplicities for $p_{T}(jet) > 75.0$ GeV.
jet multiplicities for $p_{T}(jet) > 100.0$ GeV.
jet multiplicities for $p_{T}(jet) > 100.0$ GeV.
Covariance matrix of jet multiplicities with different pT(jet) thresholds.
Covariance matrix of jet multiplicities with different pT(jet) thresholds.
Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{high})$.
Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{high})$.
Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{high})$.
Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{high})$.
Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{low})$.
Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{low})$.
Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{low})$.
Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{low})$.
Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$.
Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$.
Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$.
Absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$.
Covariance matrix of absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$.
Covariance matrix of absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$.
Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{l})$.
Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{l})$.
Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{l})$.
Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{l})$.
Absolute cross section at the parton level as a function of $|y(\text{t}_\text{l})|$.
Absolute cross section at the parton level as a function of $|y(\text{t}_\text{l})|$.
Covariance matrix of absolute cross section at the parton level as a function of $|y(\text{t}_\text{l})|$.
Covariance matrix of absolute cross section at the parton level as a function of $|y(\text{t}_\text{l})|$.
Absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$.
Absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$.
Covariance matrix of absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$.
Covariance matrix of absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$.
Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.
Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.
Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.
Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.
Absolute cross section at the parton level as a function of $|y(\text{t}\bar{\text{t}})|$.
Absolute cross section at the parton level as a function of $|y(\text{t}\bar{\text{t}})|$.
Covariance matrix of absolute cross section at the parton level as a function of $|y(\text{t}\bar{\text{t}})|$.
Covariance matrix of absolute cross section at the parton level as a function of $|y(\text{t}\bar{\text{t}})|$.
Absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Covariance matrix of absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Covariance matrix of absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Covariance matrix of absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Covariance matrix of absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{high})$.
Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{high})$.
Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{high})$.
Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{high})$.
Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{low})$.
Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{low})$.
Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{low})$.
Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{low})$.
Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$.
Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$.
Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$.
Normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$.
Covariance matrix of normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$.
Covariance matrix of normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$.
Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{l})$.
Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{l})$.
Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{l})$.
Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{l})$.
Normalized cross section at the parton level as a function of $|y(\text{t}_\text{l})|$.
Normalized cross section at the parton level as a function of $|y(\text{t}_\text{l})|$.
Covariance matrix of normalized cross section at the parton level as a function of $|y(\text{t}_\text{l})|$.
Covariance matrix of normalized cross section at the parton level as a function of $|y(\text{t}_\text{l})|$.
Normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$.
Normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$.
Covariance matrix of normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$.
Covariance matrix of normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$.
Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.
Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.
Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.
Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.
Normalized cross section at the parton level as a function of $|y(\text{t}\bar{\text{t}})|$.
Normalized cross section at the parton level as a function of $|y(\text{t}\bar{\text{t}})|$.
Covariance matrix of normalized cross section at the parton level as a function of $|y(\text{t}\bar{\text{t}})|$.
Covariance matrix of normalized cross section at the parton level as a function of $|y(\text{t}\bar{\text{t}})|$.
Normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Covariance matrix of normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Covariance matrix of normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.
Normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Covariance matrix of normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Covariance matrix of normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.
Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.
The production of $\Upsilon$(2S) and $\Upsilon$(3S) mesons in lead-lead (PbPb) and proton-proton (pp) collisions is studied in their dimuon decay channel using the CMS detector at the LHC. The $\Upsilon$(3S) meson is observed for the first time in PbPb collisions, with a significance above five standard deviations. The ratios of yields measured in PbPb and pp collisions are reported for both the $\Upsilon$(2S) and $\Upsilon$(3S) mesons, as functions of transverse momentum and PbPb collision centrality. These ratios, when appropriately scaled, are significantly less than unity, indicating a suppression of $\Upsilon$ yields in PbPb collisions. This suppression increases from peripheral to central PbPb collisions. Furthermore, the suppression is stronger for $\Upsilon$(3S) mesons compared to $\Upsilon$(2S) mesons, extending the pattern of sequential suppression of quarkonium states in nuclear collisions previously seen for the $\psi$/J, $\psi$(2S), $\Upsilon$(1S), and $\Upsilon$(2S) mesons.
Measured $R_\text{AA}$ for the $\Upsilon(2S)$ state as functions of PbPb collision centrality, integrated over the full kinematic range $p_\text{T}$ < 30 GeV/c and |y| < 2.4. The global uncertainty "PP MB" represents the pp luminosity and PbPb $N_\text{MB}$ combined uncertainties, whereas the global uncertainty "PP 2S" corresponds to the uncertainty on the $\Upsilon(2S)$ pp yields.
Measured $R_\text{AA}$ for the $\Upsilon(3S)$ state as functions of PbPb collision centrality, integrated over the full kinematic range $p_\text{T}$ < 30 GeV/c and |y| < 2.4. The global uncertainty "PP MB" represents the pp luminosity and PbPb $N_\text{MB}$ combined uncertainties, whereas the global uncertainty "PP 3S" corresponds to the uncertainty on the $\Upsilon(3S)$ pp yields.
Measured $R_\text{AA}$ for the$\Upsilon(2S)$ state in the 0–90% centrality interval, integrated over the full kinematic range $p_\text{T}$ < 30 GeV/c and |y| < 2.4. The global uncertainty "PP MB" represents the pp luminosity and PbPb $N_\text{MB}$ combined uncertainties, whereas the global uncertainty "PP 2S" corresponds to the uncertainty on the $\Upsilon(2S)$ pp yields.
Measured $R_\text{AA}$ for the $\Upsilon(3S)$ state in the 0–90% centrality interval, integrated over the full kinematic range $p_\text{T}$ < 30 GeV/c and |y| < 2.4. The global uncertainty "PP MB" represents the pp luminosity and PbPb $N_\text{MB}$ combined uncertainties, whereas the global uncertainty "PP 3S" corresponds to the uncertainty on the$\Upsilon(3S)$ pp yields.
Measured $R_\text{AA}$ for the $\Upsilon(2S)$ state as functions of transverse momentum $p_\text{T}$, integrated over the rapidity range of |y| < 2.4 and the 0–90% centrality interval. The global uncertainty combines the uncertainties of $T_\text{AA}$ , pp luminosity, and PbPb $N_\text{MB}$.
Measured $R_\text{AA}$ for the $\Upsilon(3S)$ state as functions of transverse momentum $p_\text{T}$, integrated over the rapidity range of |y| < 2.4 and the 0–90% centrality interval. The global uncertainty combines the uncertainties of $T_\text{AA}$ , pp luminosity, and PbPb $N_\text{MB}$.
The double ratios of $\Upsilon(3S)$ /$\Upsilon(2S)$ as functions of PbPb collision centrality, integrated over the full kinematic range $p_\text{T}$ < 30 GeV/c and |y| < 2.4. The global uncertainty represents the combined systematic and statistical uncertainties from pp data.
The double ratios of $\Upsilon(3S)$ /$\Upsilon(2S)$ in the 0–90% centrality interval, integrated over the full kinematic range $p_\text{T}$ < 30 GeV/c and |y| < 2.4. The global uncertainty represents the combined systematic and statistical uncertainties from pp data.
The double ratios of $\Upsilon(3S)$ /$\Upsilon(2S)$ as functions of transverse momentum $p_\text{T}$, integrated over the rapidity range of |y| < 2.4 and the 0–90% centrality interval.
Yields for $\Upsilon(2S)$ mesons in PbPb collisions in centrality $0--90\%$ and $|y| < 2.4$, corrected for acceptance and efficiency, and normalized by the nuclear thickness function $\langle T_{AA} \rangle$ and the number of minimum bias events $N_{MB}$. The numbers are in units of pb$/GeV/c$.
Yields for $\Upsilon(2S)$ mesons in PbPb collisions in $p_\text{T} < 30GeV/c$ and $|y| < 2.4$, corrected for acceptance and efficiency, and normalized by the nuclear thickness function $\langle T_{AA} \rangle$ and the number of minimum bias events $N_{MB}$. The numbers are in units of pb$/GeV/c$.
Yields for $\Upsilon(3S)$ mesons in PbPb collisions in centrality $0--90\%$ and $|y| < 2.4$, corrected for acceptance and efficiency, and normalized by the nuclear thickness function $\langle T_{AA} \rangle$ and the number of minimum bias events $N_{MB}$. The numbers are in units of pb$/GeV/c$.
Yields for $\Upsilon(3S)$ mesons in PbPb collisions in $p_\text{T} < 30GeV/c$ and $|y| < 2.4$, corrected for acceptance and efficiency, and normalized by the nuclear thickness function $\langle T_{AA} \rangle$ and the number of minimum bias events $N_{MB}$. The numbers are in units of pb$/GeV/c$.
Yields for $\Upsilon(1S)$ mesons in PbPb collisions in $p_\text{T} < 30GeV/c$ and $|y| < 2.4$, corrected for acceptance and efficiency, and normalized by the nuclear thickness function $\langle T_{AA} \rangle$ and the number of minimum bias events $N_{MB}$. The numbers are in units of pb$/GeV/c$. The values and global integrated luminosity uncertainty are quoted from data taken from CMS in 2018 LHC PbPb run.
We present a measurement of the elastic differential cross section $d\sigma(p\bar{p}\rightarrow p\bar{p})/dt$ as a function of the four-momentum-transfer squared t. The data sample corresponds to an integrated luminosity of $\approx 31 nb^{-1}$ collected with the D0 detector using dedicated Tevatron $p\bar{p} $ Collider operating conditions at sqrt(s) = 1.96 TeV and covers the range $0.26 <|t|< 1.2 GeV^2$. For $|t|<0.6 GeV^2$, d\sigma/dt is described by an exponential function of the form $Ae^{-b|t|}$ with a slope parameter $ b = 16.86 \pm 0.10(stat) \pm 0.20(syst) GeV^{-2}$. A change in slope is observed at $|t| \approx 0.6 GeV^2$, followed by a more gradual |t| dependence with increasing values of |t|.
The $d\sigma$/$dt$ differential cross section. The statistical and systematic uncertainties are added in quadrature.
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