Showing 10 of 1944 results
The correlations between flow harmonics $v_n$ for $n=2$, 3 and 4 and mean transverse momentum $[p_\mathrm{T}]$ in $^{129}$Xe+$^{129}$Xe and $^{208}$Pb+$^{208}$Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.44$ TeV and 5.02 TeV, respectively, are measured using charged particles with the ATLAS detector. The correlations are sensitive to the shape and size of the initial geometry, nuclear deformation, and initial momentum anisotropy. The effects from non-flow and centrality fluctuations are minimized, respectively, via a subevent cumulant method and event activity selection based on particle production in the very forward rapidity. The results show strong dependences on centrality, harmonic number $n$, $p_{\mathrm{T}}$ and pseudorapidity range. Current models describe qualitatively the overall centrality- and system-dependent trends but fail to quantitatively reproduce all the data. In the central collisions, where models generally show good agreement, the $v_2$-$[p_\mathrm{T}]$ correlations are sensitive to the triaxiality of the quadruple deformation. The comparison of model to the Pb+Pb and Xe+Xe data suggests that the $^{129}$Xe nucleus is a highly deformed triaxial ellipsoid that is neither a prolate nor an oblate shape. This provides strong evidence for a triaxial deformation of $^{129}$Xe nucleus using high-energy heavy-ion collision.
$\rho_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{2}$ Standard method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{2}$ Two_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{2}$ Three_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{3}$ Standard method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{3}$ Two_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{3}$ Three_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{4}$ Standard method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{4}$ Two_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{4}$ Three_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{2}$ Three_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{3}$ Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{3}$ Combined_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{4}$ Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{4}$ Combined_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{2}$ Three_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{3}$ Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{3}$ Combined_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{4}$ Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{4}$ Combined_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{3}$ Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{3}$ Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{4}$ Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{4}$ Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{2}$ Three_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{2}$ Three_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{2}$ Three_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.3< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{3}$ Combined_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{3}$ Combined_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{3}$ Combined_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.3< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{4}$ Combined_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{4}$ Combined_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{4}$ Combined_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.3< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{3}$ Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{3}$ Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{4}$ Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{4}$ Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$Cov_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$Cov_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$Cov_{3}$ Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$Cov_{3}$ Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$Cov_{4}$ Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$Cov_{4}$ Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N_{ch}^{rec}$ based Centrality
$\rho_{3}$ Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{3}$ Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N_{ch}^{rec}$ based Centrality
$\rho_{4}$ Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{4}$ Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N_{ch}^{rec}$ based Centrality
$\rho_{2}$ Three_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{2}$ Three_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N_{ch}^{rec}$ based Centrality
$\rho_{3}$ Combined_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{3}$ Combined_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N_{ch}^{rec}$ based Centrality
$\rho_{4}$ Combined_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality
$\rho_{4}$ Combined_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N_{ch}^{rec}$ based Centrality
$\rho_{2}$ for peripheral events, Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N_{ch}^{rec}$ based Centrality,
$\rho_{2}$ for peripheral events, Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $N_{ch}^{rec}$ based Centrality,
$\rho_{2}$ for peripheral events, Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{2}$ for peripheral events, Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{2}$ for peripheral events, Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N_{ch}^{rec}$ based Centrality,
$\rho_{2}$ for peripheral events, Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $N_{ch}^{rec}$ based Centrality,
$\rho_{2}$ for peripheral events, Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{2}$ for peripheral events, Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{2}$ for peripheral events, Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N_{ch}^{rec}$ based Centrality,
$\rho_{2}$ for peripheral events, Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $N_{ch}^{rec}$ based Centrality,
$\rho_{2}$ for peripheral events, Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{2}$ for peripheral events, Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{2}$, Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{2}$, Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{3}$, Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{3}$, Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{2}$, Three_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{2}$, Three_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{3}$, Combined_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{3}$, Combined_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{2}$ for central events, Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{2}$ for central events, Three_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{2}$ for central events, Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{2}$ for central events, Three_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{2}$ ratio between Xe+Xe 5.44 TeV and Pb+Pb 5.02 TeV for central events, Three_subevent method, for , $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{2}$ ratio between Xe+Xe 5.44 TeV and Pb+Pb 5.02 TeV for central events, Three_subevent method, for , $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\Sigma E_{T}$ vs $N^{rec}_{ch}$ for Pb+Pb 5.02 TeV
$\Sigma E_{T}$ vs $N^{rec}_{ch}$ for Xe+Xe 5.44 TeV
$\rho_{2}$ ratio between Xe+Xe 5.44 TeV and Pb+Pb 5.02 TeV for central events, Standard method, for , $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{2}$ ratio between Xe+Xe 5.44 TeV and Pb+Pb 5.02 TeV for central events, Standard method, for , $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{2}$ ratio between Xe+Xe 5.44 TeV and Pb+Pb 5.02 TeV for central events, Combined_subevent method, for , $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{2}$ ratio between Xe+Xe 5.44 TeV and Pb+Pb 5.02 TeV for central events, Combined_subevent method, for , $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{2}$ ratio between Xe+Xe 5.44 TeV and Pb+Pb 5.02 TeV for central events, Three_subevent method, for , $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality,
$\rho_{2}$ ratio between Xe+Xe 5.44 TeV and Pb+Pb 5.02 TeV for central events, Three_subevent method, for , $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality,
$\rho_{2}$ ratio between Xe+Xe 5.44 TeV and Pb+Pb 5.02 TeV for central events, Combined_subevent method, for , $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality,
$\rho_{2}$ ratio between Xe+Xe 5.44 TeV and Pb+Pb 5.02 TeV for central events, Combined_subevent method, for , $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality,
$\rho_{3}$ for central events, Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{3}$ for central events, Combined_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{3}$ for central events, Combined_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{3}$ for central events, Combined_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{3}$ ratio between Xe+Xe 5.44 TeV and Pb+Pb 5.02 TeV for central events, Standard method, for , $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{3}$ ratio between Xe+Xe 5.44 TeV and Pb+Pb 5.02 TeV for central events, Standard method, for , $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{3}$ ratio between Xe+Xe 5.44 TeV and Pb+Pb 5.02 TeV for central events, Combined_subevent method, for , $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{3}$ ratio between Xe+Xe 5.44 TeV and Pb+Pb 5.02 TeV for central events, Combined_subevent method, for , $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality,
$\rho_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$
$\rho_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$
$\rho_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$
$\rho_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$
$\rho_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$
$\rho_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$
$\rho_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$
$\rho_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$
$\rho_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$
$\rho_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$
$\rho_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$
$\rho_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$
$\rho_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$
$\rho_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$
$\rho_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$
$\rho_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$
$\rho_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$
$\rho_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$\rho_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$
$\rho_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$
$\rho_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$
$\rho_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$
$\rho_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$
$\rho_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$
$\rho_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$
$\rho_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$
$\rho_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$
$\rho_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$
$\rho_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$
$\rho_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$
$\rho_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$
$\rho_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$
$\rho_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$
$\rho_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$
$\rho_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$
$\rho_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$
$\rho_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$
$\rho_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$
$\rho_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$
$\rho_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$
$\rho_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$
$\rho_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$
$\rho_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$
$\rho_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$
$\rho_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$
$\rho_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$
$\rho_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$
$\rho_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$
$\rho_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$
$\rho_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$
$\rho_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$\rho_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<1.0, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$Cov_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$.
$Cov_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$.
$Cov_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$.
$Cov_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$.
$Cov_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$.
$Cov_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$.
$Cov_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$.
$Cov_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$.
$Cov_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$.
$Cov_{2}$ Standard method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$.
$Cov_{2}$ Two_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$.
$Cov_{2}$ Three_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$.
$Cov_{3}$ Standard method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$.
$Cov_{3}$ Two_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$.
$Cov_{3}$ Three_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$.
$Cov_{4}$ Standard method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$.
$Cov_{4}$ Two_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$.
$Cov_{4}$ Three_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$.
$Cov_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{2}$ Standard method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{2}$ Two_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{2}$ Three_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{3}$ Standard method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{3}$ Two_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{3}$ Three_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{4}$ Standard method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{4}$ Two_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{4}$ Three_subevent method, for Xe+Xe 5.44 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $\Sigma E_{T}$ based Centrality.
$Cov_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$Cov_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$Cov_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$Cov_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$Cov_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$Cov_{3}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$Cov_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$Cov_{4}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$Cov_{4}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$Cov_{2}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$Cov_{2}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$Cov_{2}$ Three_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$Cov_{3}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality.
$Cov_{3}$ Two_subevent method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <5.0 GeV vs $N^{rec}_{ch}$ based Centrality.
Searches for new phenomena inspired by supersymmetry in final states containing an $e^+e^-$ or $\mu^+\mu^-$ pair, jets, and missing transverse momentum are presented. These searches make use of proton-proton collision data with an integrated luminosity of 139 $\text{fb}^{-1}$, collected during 2015-2018 at a centre-of-mass energy $\sqrt{s}=13 $TeV by the ATLAS detector at the Large Hadron Collider. Two searches target the pair production of charginos and neutralinos. One uses the recursive-jigsaw reconstruction technique to follow up on excesses observed in 36.1 $\text{fb}^{-1}$ of data, and the other uses conventional event variables. The third search targets pair production of coloured supersymmetric particles (squarks or gluinos) decaying through the next-to-lightest neutralino $(\tilde\chi_2^0)$ via a slepton $(\tilde\ell)$ or $Z$ boson into $\ell^+\ell^-\tilde\chi_1^0$, resulting in a kinematic endpoint or peak in the dilepton invariant mass spectrum. The data are found to be consistent with the Standard Model expectations. Results are interpreted using simplified models and exclude masses up to 900 GeV for electroweakinos, 1550 GeV for squarks, and 2250 GeV for gluinos.
- - - - - - - - Overview of HEPData Record - - - - - - - - <br/><br/> <b>EWK SR distributions:</b> <a href="116034?version=1&table=Figure 11a">SR-High_8-EWK</a>; <a href="116034?version=1&table=Figure 11b">SR-ℓℓ𝑏𝑏-EWK</a>; <a href="116034?version=1&table=Figure 11c">SR-Int-EWK</a>; <a href="116034?version=1&table=Figure 11d">SR-Low-EWK</a>; <a href="116034?version=1&table=Figure 11e">SR-OffShell-EWK</a><br/><br/> <b>Strong SR distributions:</b> <a href="116034?version=1&table=Figure 13a">SRC-STR</a>; <a href="116034?version=1&table=Figure 13b">SRLow-STR</a>; <a href="116034?version=1&table=Figure 13c">SRMed-STR</a>; <a href="116034?version=1&table=Figure 13d">SRHigh-STR</a><br/><br/> <b>RJR SR Yields:</b> <a href="116034?version=1&table=Table 16">SR2l-Low-RJR, SR2l-ISR-RJR</a><br/><br/> <b>EWK SR Yields:</b> <a href="116034?version=1&table=Table 18">SR-High_16a-EWK, SR-High_8a-EWK, SR-1J-High-EWK, SR-ℓℓ𝑏𝑏-EWK, SR-High_16b-EWK, SR-High_8b-EWK</a>; <a href="116034?version=1&table=Table 19">SR-Int_a-EWK, SR-Low_a-EWK, SR-Low-2-EWK, SR-OffShell_a-EWK, SR-Int_b-EWK, SR-Low_b-EWK, SR-OffShell_b-EWK </a><br/><br/> <b>Strong SR Yields:</b> <a href="116034?version=1&table=Table 21">SRC-STR, SRLow-STR, SRMed-STR, SRHigh-STR</a>; <a href="116034?version=1&table=Table 22">SRZLow-STR, SRZMed-STR, SRZHigh-STR</a><br/><br/> <b>C1N2 Model Limits:</b> <a href="116034?version=1&table=Table 15a C1N2 Observed Limit">Obs</a>; <a href="116034?version=1&table=Table 15a C1N2 Expected Limit">Exp</a>; <a href="116034?version=1&table=Auxiliary Figure 34a C1N2 Expected XS Upper Limit">Upper Limits</a><br/><br/> <b>GMSB Model Limits:</b> <a href="116034?version=1&table=Table 15b GMSB Observed Limit">Obs</a>; <a href="116034?version=1&table=Table 15b GMSB Expected Limit">Exp</a>; <a href="116034?version=1&table=Auxiliary Figure 34b GMSB Expected XS Upper Limit">Upper Limits</a><br/><br/> <b>Gluon-Slepton Model Limits:</b> <a href="116034?version=1&table=Figure 16a Observed Limit">Obs</a>; <a href="116034?version=1&table=Figure 16a Expected Limit">Exp</a>; <a href="116034?version=1&table=Auxiliary Figure 23a XS Upper Limit">Upper Limits</a><br/><br/> <b>Gluon-Z* Model Limits:</b> <a href="116034?version=1&table=Figure 16b Observed Limit">Obs</a>; <a href="116034?version=1&table=Figure 16b Expected Limit">Exp</a>; <a href="116034?version=1&table=Auxiliary Figure 23b XS Upper Limit">Upper Limits</a><br/><br/> <b>Squark-Z* Model Limits:</b> <a href="116034?version=1&table=Figure 16c Observed Limit">Obs</a>; <a href="116034?version=1&table=Figure 16c Expected Limit">Exp</a>; <a href="116034?version=1&table=Auxiliary Figure 23c XS Upper Limit">Upper Limits</a><br/><br/> <b>EWK VR distributions:</b> <a href="116034?version=1&table=Figure 4a S_ETmiss in VR-High-Sideband-EWK">VR-High-Sideband-EWK</a>; <a href="116034?version=1&table=Figure 4b S_Etmiss in VR-High-R-EWK">VR-High-R-EWK</a>; <a href="116034?version=1&table=Figure 4c S_Etmiss in VR-1J-High-EWK">VR-1J-High-EWK</a>; <a href="116034?version=1&table=Figure 4d S_Etmiss in VR-llbb-EWK">VR-ℓℓ𝑏𝑏-EWK</a>; <a href="116034?version=1&table=Figure 5a S_Etmiss in VR-Int-EWK">VR-Int-EWK</a>; <a href="116034?version=1&table=Figure 5b S_Etmiss in VR-Low-EWK">VR-Low-EWK</a>; <a href="116034?version=1&table=Figure 5c S_Etmiss in VR-Low-2-EWK">VR-Low-2-EWK</a>; <a href="116034?version=1&table=Figure 5d S_Etmiss in VR-OffShell-EWK">VR-OffShell-EWK</a><br/><br/> <b>Strong VR distributions:</b> <a href="116034?version=1&table=Figure 6a">VRC-STR</a>; <a href="116034?version=1&table=Figure 6b">VRLow-STR</a>; <a href="116034?version=1&table=Figure 6c">VRMed-STR</a>; <a href="116034?version=1&table=Figure 6d">VRHigh-STR</a>; <a href="116034?version=1&table=Figure 8">VR3L-STR</a><br/><br/> <b>Other Strong distributions:</b> <a href="116034?version=1&table=Auxiliary Figure 17a">SRLow-STR + VRLow-STR</a><br/><br/> <b>Other EWK distributions:</b> <a href="116034?version=1&table=Auxiliary Figure 33a Mjj in CR-Z-EWK and SR-Low-EWK">CR-Z-EWK + SR-Low-EWK</a>; <a href="116034?version=1&table=Auxiliary Figure 33b S_ETmiss in CR-Z-met-EWK">CR-Z-met-EWK</a><br/><br/> <b>Strong Signal Cutflows:</b> <a href="116034?version=1&table=Auxiliary Table 30-31 SRC-STR Cutflow">SRC-STR GG_N2_ZN1</a>; <a href="116034?version=1&table=Auxiliary Table 30-31 SRMed-STR Cutflow">SRC-STR SS_N2_ZN1</a>; <a href="116034?version=1&table=Auxiliary Table 30-31 SRLow-STR Cutflow">SRLow-STR GG_N2_SLN1</a>; <a href="116034?version=1&table=Auxiliary Table 30-31 SRHigh-STR Cutflow">SRC-STR GG_N2_SLN1</a>; <a href="116034?version=1&table=Auxiliary Table 30-31 SRZLow-STR Cutflow">SRZLow-STR SS_N2_ZN1</a>; <a href="116034?version=1&table=Auxiliary Table 30-31 SRZMed-STR Cutflow">SRZMed-STR SS_N2_ZN1</a>; <a href="116034?version=1&table=Auxiliary Table 30-31 SRZHigh-STR Cutflow">SRZHigh-STR SS_N2_ZN1</a><br/><br/> <b>EWK Signal Cutflows:</b> <a href="116034?version=1&table=Auxiliary Table 36 SR-OffShell_a-EWK Cutflow"> SR-OffShell_a-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 37 SR-OffShell_b-EWK Cutflow"> SR-OffShell_b-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 38 SR-Low_a-EWK Cutflow"> SR-Low_a-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 39 SR-Low_b-EWK Cutflow"> SR-Low_b-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 40 SR-Low-2-EWK Cutflow"> SR-Low-2-E</a>; <a href="116034?version=1&table=Auxiliary Table 41 SR-Int_a-EWK Cutflow"> SR-Int_a-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 42 SR-Int_b-EWK Cutflow"> SR-Int_b-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 43 SR-High_16a-EWK Cutflow"> SR-High_16a-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 44 SR-High_16b-EWK Cutflow"> SR-High_16b-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 45 SR-High_8a-EWK Cutflow"> SR-High_8a-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 46 SR-High_8b-EWK Cutflow"> SR-High_8b-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 47 SR-1J-High-EWK Cutflow"> SR-1J-Hig</a>; <a href="116034?version=1&table=Auxiliary Table 48 SR-llbb-EWK Cutflow"> SR-llbb-EWK</a><br/><br/> <b>EWK Signal Number of MC Events:</b> <a href="116034?version=1&table=Auxiliary Table 36 SR-OffShell_a-EWK Generated"> SR-OffShell_a-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 37 SR-OffShell_b-EWK Generated"> SR-OffShell_b-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 38 SR-Low_a-EWK Generated"> SR-Low_a-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 39 SR-Low_b-EWK Generated"> SR-Low_b-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 40 SR-Low-2-EWK Generated"> SR-Low-2-E</a>; <a href="116034?version=1&table=Auxiliary Table 41 SR-Int_a-EWK Generated"> SR-Int_a-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 42 SR-Int_b-EWK Generated"> SR-Int_b-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 43 SR-High_16a-EWK Generated"> SR-High_16a-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 44 SR-High_16b-EWK Generated"> SR-High_16b-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 45 SR-High_8a-EWK Generated"> SR-High_8a-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 46 SR-High_8b-EWK Generated"> SR-High_8b-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 47 SR-1J-High-EWK Generated"> SR-1J-Hig</a>; <a href="116034?version=1&table=Auxiliary Table 48 SR-llbb-EWK Generated"> SR-llbb-EWK</a><br/><br/> <b>SRC-STR Signal Acceptance:</b> <a href="116034?version=1&table=GG_N2_SLN1 acc in SRC">GG_N2_SLN1</a>; <a href="116034?version=1&table=GG_N2_ZN1 acc in SRC">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 acc in SRC">SS_N2_ZN1</a><br/><br/> <b>SRLow-STR Signal Acceptance:</b> <a href="116034?version=1&table=GG_N2_SLN1 acc in SRLow">GG_N2_SLN1</a>; <a href="116034?version=1&table=GG_N2_ZN1 acc in SRLow">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 acc in SRLow">SS_N2_ZN1</a><br/><br/> <b>SRMed-STR Signal Acceptance:</b> <a href="116034?version=1&table=GG_N2_SLN1 acc in SRMed">GG_N2_SLN1</a>; <a href="116034?version=1&table=GG_N2_ZN1 acc in SRMed">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 acc in SRMed">SS_N2_ZN1</a><br/><br/> <b>SRHigh-STR Signal Acceptance:</b> <a href="116034?version=1&table=GG_N2_SLN1 acc in SRHigh">GG_N2_SLN1</a>; <a href="116034?version=1&table=GG_N2_ZN1 acc in SRHigh">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 acc in SRHigh">SS_N2_ZN1</a><br/><br/> <b>SRZLow-STR Signal Acceptance:</b> <a href="116034?version=1&table=GG_N2_ZN1 acc in SRZLow">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 acc in SRZLow">SS_N2_ZN1</a><br/><br/> <b>SRZMed-STR Signal Acceptance:</b> <a href="116034?version=1&table=GG_N2_ZN1 acc in SRZMed">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 acc in SRZMed">SS_N2_ZN1</a><br/><br/> <b>SRZHigh-STR Signal Acceptance:</b> <a href="116034?version=1&table=GG_N2_ZN1 acc in SRZHigh">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 acc in SRZHigh">SS_N2_ZN1</a><br/><br/> <b>SRC-STR Signal Efficiency:</b> <a href="116034?version=1&table=GG_N2_SLN1 eff in SRC">GG_N2_SLN1</a>; <a href="116034?version=1&table=GG_N2_ZN1 eff in SRC">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 eff in SRC">SS_N2_ZN1</a><br/><br/> <b>SRLow-STR Signal Efficiency:</b> <a href="116034?version=1&table=GG_N2_SLN1 eff in SRLow">GG_N2_SLN1</a>; <a href="116034?version=1&table=GG_N2_ZN1 eff in SRLow">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 eff in SRLow">SS_N2_ZN1</a><br/><br/> <b>SRMed-STR Signal Efficiency:</b> <a href="116034?version=1&table=GG_N2_SLN1 eff in SRMed">GG_N2_SLN1</a>; <a href="116034?version=1&table=GG_N2_ZN1 eff in SRMed">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 eff in SRMed">SS_N2_ZN1</a><br/><br/> <b>SRHigh-STR Signal Efficiency:</b> <a href="116034?version=1&table=GG_N2_SLN1 eff in SRHigh">GG_N2_SLN1</a>; <a href="116034?version=1&table=GG_N2_ZN1 eff in SRHigh">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 eff in SRHigh">SS_N2_ZN1</a><br/><br/> <b>SRZLow-STR Signal Efficiency:</b> <a href="116034?version=1&table=GG_N2_ZN1 eff in SRZLow">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 eff in SRZLow">SS_N2_ZN1</a><br/><br/> <b>SRZMed-STR Signal Efficiency:</b> <a href="116034?version=1&table=GG_N2_ZN1 eff in SRZMed">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 eff in SRZMed">SS_N2_ZN1</a><br/><br/> <b>SRZHigh-STR Signal Efficiency:</b> <a href="116034?version=1&table=GG_N2_ZN1 eff in SRZHigh">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 eff in SRZHigh">SS_N2_ZN1</a><br/><br/> <b>SR-OffShell_a-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in SR-OffShell_a-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in SR-OffShell_a-EWK">C1N2</a>; <br/><br/> <b>SR-OffShell_b-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in SR-OffShell_b-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in SR-OffShell_b-EWK">C1N2</a>; <br/><br/> <b>SR-Low_a-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in C1N2 acc in SR-Low_a-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in C1N2 acc in SR-Low_a-EWK">C1N2</a>; <br/><br/> <b>SR-Low_b-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in SR-Low_b-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in SR-Low_b-EWK">C1N2</a>; <br/><br/> <b>SR-Int_a-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in SR-Int_a-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in SR-Int_a-EWK">C1N2</a>; <br/><br/> <b>SR-Int_b-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in SR-Int_b-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in SR-Int_b-EWK">C1N2</a>; <br/><br/> <b>SR-High_16a-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in SR-High_16a-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in SR-High_16a-EWK">C1N2</a>; <br/><br/> <b>SR-High_16b-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in SR-High_16b-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in SR-High_16b-EWK">C1N2</a>; <br/><br/> <b>SR-High_8a-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in SR-High_8a-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in SR-High_8a-EWK">C1N2</a>; <br/><br/> <b>SR-High_8b-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in SR-High_8b-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in SR-High_8b-EWK">C1N2</a>; <br/><br/> <b>SR-1J-High-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in SR-1J-High-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in SR-1J-High-EWK">C1N2</a>; <br/><br/> <b>SR-llbb-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in SR-llbb-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in SR-llbb-EWK">C1N2</a>; <br/><br/> <b>SR-OffShell_a-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in SR-OffShell_a-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in SR-OffShell_a-EWK">C1N2</a>; <br/><br/> <b>SR-OffShell_b-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in SR-OffShell_b-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in SR-OffShell_b-EWK">C1N2</a>; <br/><br/> <b>SR-Low_a-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in C1N2 eff in SR-Low_a-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in C1N2 eff in SR-Low_a-EWK">C1N2</a>; <br/><br/> <b>SR-Low_b-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in SR-Low_b-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in SR-Low_b-EWK">C1N2</a>; <br/><br/> <b>SR-Int_a-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in SR-Int_a-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in SR-Int_a-EWK">C1N2</a>; <br/><br/> <b>SR-Int_b-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in SR-Int_b-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in SR-Int_b-EWK">C1N2</a>; <br/><br/> <b>SR-High_16a-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in SR-High_16a-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in SR-High_16a-EWK">C1N2</a>; <br/><br/> <b>SR-High_16b-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in SR-High_16b-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in SR-High_16b-EWK">C1N2</a>; <br/><br/> <b>SR-High_8a-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in SR-High_8a-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in SR-High_8a-EWK">C1N2</a>; <br/><br/> <b>SR-High_8b-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in SR-High_8b-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in SR-High_8b-EWK">C1N2</a>; <br/><br/> <b>SR-1J-High-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in SR-1J-High-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in SR-1J-High-EWK">C1N2</a>; <br/><br/> <b>SR-llbb-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in SR-llbb-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in SR-llbb-EWK">C1N2</a>; <br/><br/> <b>Truth Code snippets</b>, <b>SLHA files</b>, and <b>PYHF json likelihoods</b> are available under "Resources" (purple button on the left) ---- Record created with hepdata_lib 0.7.0: https://zenodo.org/record/4946277 and PYHF: https://doi.org/10.5281/zenodo.1169739
Breakdown of expected and observed yields in the two recursive-jigsaw reconstruction signal regions after a simultaneous fit of the the CRs. The two sets of regions are fit separately. The uncertainties include both statistical and systematic sources.
Breakdown of expected and observed yields in the electroweak search High and $\ell\ell bb$ signal regions after a simultaneous fit to the signal regions and control regions. All statistical and systematic uncertainties are included.
Breakdown of expected and observed yields in the electroweak search Int, Low, and OffShell signal regions after a simultaneous fit to the signal regions and control regions. All statistical and systematic uncertainties are included.
Breakdown of expected and observed yields in the four edge signal regions, integrated over the $m_{\ell\ell}$ distribution after a separate simultaneous fit to each signal region and control region pair. The uncertainties include both the statistical and systematic sources.
Breakdown of expected and observed yields in the three on-$Z$ signal regions after a separate simultaneous fit to each signal region and control region pair. The uncertainties include both the statistical and systematic sources.
Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-High-Sideband-EWK (top-left), VR-High-R-EWK (top-right), VR-1J-High-EWK (bottom-left), and VR-$\ell\ell bb$-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.
Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-High-Sideband-EWK (top-left), VR-High-R-EWK (top-right), VR-1J-High-EWK (bottom-left), and VR-$\ell\ell bb$-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.
Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-High-Sideband-EWK (top-left), VR-High-R-EWK (top-right), VR-1J-High-EWK (bottom-left), and VR-$\ell\ell bb$-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.
Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-High-Sideband-EWK (top-left), VR-High-R-EWK (top-right), VR-1J-High-EWK (bottom-left), and VR-$\ell\ell bb$-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.
Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-Int-EWK (top-left), VR-Low-EWK (top-right), VR-Low-2-EWK (bottom-left), and VR-OffShell-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.
Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-Int-EWK (top-left), VR-Low-EWK (top-right), VR-Low-2-EWK (bottom-left), and VR-OffShell-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.
Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-Int-EWK (top-left), VR-Low-EWK (top-right), VR-Low-2-EWK (bottom-left), and VR-OffShell-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.
Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-Int-EWK (top-left), VR-Low-EWK (top-right), VR-Low-2-EWK (bottom-left), and VR-OffShell-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.
Observed and expected dilepton mass distributions in VRC-STR (top-left), VRLow-STR (top-right), VRMed-STR (bottom-left), and VRHigh-STR (bottom-right). Each validation region is fit separately with the corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The entries are normalized to the bin width, and the last bin is the overflow.
Observed and expected dilepton mass distributions in VRC-STR (top-left), VRLow-STR (top-right), VRMed-STR (bottom-left), and VRHigh-STR (bottom-right). Each validation region is fit separately with the corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The entries are normalized to the bin width, and the last bin is the overflow.
Observed and expected dilepton mass distributions in VRC-STR (top-left), VRLow-STR (top-right), VRMed-STR (bottom-left), and VRHigh-STR (bottom-right). Each validation region is fit separately with the corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The entries are normalized to the bin width, and the last bin is the overflow.
Observed and expected dilepton mass distributions in VRC-STR (top-left), VRLow-STR (top-right), VRMed-STR (bottom-left), and VRHigh-STR (bottom-right). Each validation region is fit separately with the corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The entries are normalized to the bin width, and the last bin is the overflow.
Observed and expected jet multiplicity in VRLow-STR (top-left), VRMed-STR (top-right), and VRHigh-STR (bottom) after a fit performed on the $m_{\ell\ell}$ distribution and corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The last bin contains the overflow.
Observed and expected jet multiplicity in VRLow-STR (top-left), VRMed-STR (top-right), and VRHigh-STR (bottom) after a fit performed on the $m_{\ell\ell}$ distribution and corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The last bin contains the overflow.
Observed and expected jet multiplicity in VRLow-STR (top-left), VRMed-STR (top-right), and VRHigh-STR (bottom) after a fit performed on the $m_{\ell\ell}$ distribution and corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The last bin contains the overflow.
Observed and expected dilepton mass distributions in VR3L-STR without a fit to the data. The 'Other' category includes the negligible contributions from $t\bar{t}$ and $Z/\gamma^*$+jets processes. The hatched band contains the statistical uncertainty and the theoretical systematic uncertainties of the $WZ$/$ZZ$ prediction, which are the dominant sources of uncertainty. No fit is performed. The last bin contains the overflow.
Observed and expected distributions in five EWK search regions after a simultaneous fit to the SR and CR. In the top row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-High_8-EWK and $m_{bb}$ in SR-$\ell\ell bb$-EWK. In the middle row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Int-EWK and $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Low-EWK. In the bottom row is $m_{\ell\ell}$ in SR-OffShell-EWK. Overlaid are example C1N2 and GMSB signal models, where the numbers in the brackets indicate the masses, in $\mathrm{GeV}$, of the $\tilde{\chi}_1^\pm$ and $\tilde{\chi}_2^0$ or the mass of the $\tilde{\chi}_1^0$ and branching ratio to the Higgs boson respectively. All statistical and systematic uncertainties are included in the hatched bands. The last bin includes the overflow.
Observed and expected distributions in five EWK search regions after a simultaneous fit to the SR and CR. In the top row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-High_8-EWK and $m_{bb}$ in SR-$\ell\ell bb$-EWK. In the middle row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Int-EWK and $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Low-EWK. In the bottom row is $m_{\ell\ell}$ in SR-OffShell-EWK. Overlaid are example C1N2 and GMSB signal models, where the numbers in the brackets indicate the masses, in $\mathrm{GeV}$, of the $\tilde{\chi}_1^\pm$ and $\tilde{\chi}_2^0$ or the mass of the $\tilde{\chi}_1^0$ and branching ratio to the Higgs boson respectively. All statistical and systematic uncertainties are included in the hatched bands. The last bin includes the overflow.
Observed and expected distributions in five EWK search regions after a simultaneous fit to the SR and CR. In the top row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-High_8-EWK and $m_{bb}$ in SR-$\ell\ell bb$-EWK. In the middle row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Int-EWK and $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Low-EWK. In the bottom row is $m_{\ell\ell}$ in SR-OffShell-EWK. Overlaid are example C1N2 and GMSB signal models, where the numbers in the brackets indicate the masses, in $\mathrm{GeV}$, of the $\tilde{\chi}_1^\pm$ and $\tilde{\chi}_2^0$ or the mass of the $\tilde{\chi}_1^0$ and branching ratio to the Higgs boson respectively. All statistical and systematic uncertainties are included in the hatched bands. The last bin includes the overflow.
Observed and expected distributions in five EWK search regions after a simultaneous fit to the SR and CR. In the top row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-High_8-EWK and $m_{bb}$ in SR-$\ell\ell bb$-EWK. In the middle row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Int-EWK and $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Low-EWK. In the bottom row is $m_{\ell\ell}$ in SR-OffShell-EWK. Overlaid are example C1N2 and GMSB signal models, where the numbers in the brackets indicate the masses, in $\mathrm{GeV}$, of the $\tilde{\chi}_1^\pm$ and $\tilde{\chi}_2^0$ or the mass of the $\tilde{\chi}_1^0$ and branching ratio to the Higgs boson respectively. All statistical and systematic uncertainties are included in the hatched bands. The last bin includes the overflow.
Observed and expected distributions in five EWK search regions after a simultaneous fit to the SR and CR. In the top row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-High_8-EWK and $m_{bb}$ in SR-$\ell\ell bb$-EWK. In the middle row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Int-EWK and $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Low-EWK. In the bottom row is $m_{\ell\ell}$ in SR-OffShell-EWK. Overlaid are example C1N2 and GMSB signal models, where the numbers in the brackets indicate the masses, in $\mathrm{GeV}$, of the $\tilde{\chi}_1^\pm$ and $\tilde{\chi}_2^0$ or the mass of the $\tilde{\chi}_1^0$ and branching ratio to the Higgs boson respectively. All statistical and systematic uncertainties are included in the hatched bands. The last bin includes the overflow.
Observed and expected dilepton mass distributions in SRC-STR (top-left), SRLow-STR (top-right), SRMed-STR (bottom-left), and SRHigh-STR (bottom-right), with the binning used for interpretations after a separate simultaneous fit to each signal region and control region pair. The red dashed lines are example signal models overlaid on the figure. All statistical and systematic uncertainties are included in the hatched bands. The last bins are the overflow.
Observed and expected dilepton mass distributions in SRC-STR (top-left), SRLow-STR (top-right), SRMed-STR (bottom-left), and SRHigh-STR (bottom-right), with the binning used for interpretations after a separate simultaneous fit to each signal region and control region pair. The red dashed lines are example signal models overlaid on the figure. All statistical and systematic uncertainties are included in the hatched bands. The last bins are the overflow.
Observed and expected dilepton mass distributions in SRC-STR (top-left), SRLow-STR (top-right), SRMed-STR (bottom-left), and SRHigh-STR (bottom-right), with the binning used for interpretations after a separate simultaneous fit to each signal region and control region pair. The red dashed lines are example signal models overlaid on the figure. All statistical and systematic uncertainties are included in the hatched bands. The last bins are the overflow.
Observed and expected dilepton mass distributions in SRC-STR (top-left), SRLow-STR (top-right), SRMed-STR (bottom-left), and SRHigh-STR (bottom-right), with the binning used for interpretations after a separate simultaneous fit to each signal region and control region pair. The red dashed lines are example signal models overlaid on the figure. All statistical and systematic uncertainties are included in the hatched bands. The last bins are the overflow.
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294]. The grey numbers indicate the observed 95\% CLs upper limit on the cross section.
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294]. The grey numbers indicate the observed 95$\%$ CLs upper limit on the cross section.
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$ ilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$ ilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$ ilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$ ilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].The grey numbers indicated the observed 95\% CL upper limit on the cross section.
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].The grey numbers indicated the observed 95\% CL upper limit on the cross section.
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].The grey numbers indicated the observed 95\% CL upper limit on the cross section.
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
The combined $E_{\mathrm{T}}^{\mathrm{miss}}$ distribution of VRLow-STR and SRLow-STR (left), and the same region with the $\Delta\phi(\boldsymbol{j}_{1,2},\boldsymbol{\mathit{p}}_{ ext{T}}^{ ext{miss}})<0.4$ requirement, used as a control region to normalize the $Z/\gamma^*+\mathrm{jets}$ process (right). Separate fits for the SR and VR are performed, as for the results in the paper, and the resulting distributions are merged. All statistical and systematic uncertainties are included in the hatched bands. The last bins contain the overflow.
Cutflow of expected events in the four Strong search edge signal regions. `Leptons' refers to electrons and muons only. The gluino-$Z^{(*)}$ model with $m_{ ilde{g}}=800~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRC-STR with 60000 Monte Carlo (MC) events generated. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRMed-STR with 30000 (MC) events generated. The gluino-slepton model with $m_{ ilde{g}}=2~TeV$ and $m_{ ilde{\ell}}=1.3~TeV$ is used for SRLow-STR and SRHigh-STR with 30000 MC events generated. The Generator Filter requires two 5~GeV leptons and 100~GeV of \met. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~GeV$ or at least one lepton with $p_{\mathrm{T}}>25~GeV$ and a photon with $p_{\mathrm{T}}>40~GeV$, with all objects within $|\eta|=2.6$.
Cutflow of expected events in the four Strong search edge signal regions. `Leptons' refers to electrons and muons only. The gluino-$Z^{(*)}$ model with $m_{ ilde{g}}=800~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRC-STR with 60000 Monte Carlo (MC) events generated. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRMed-STR with 30000 (MC) events generated. The gluino-slepton model with $m_{ ilde{g}}=2~TeV$ and $m_{ ilde{\ell}}=1.3~TeV$ is used for SRLow-STR and SRHigh-STR with 30000 MC events generated. The Generator Filter requires two 5~GeV leptons and 100~GeV of \met. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~GeV$ or at least one lepton with $p_{\mathrm{T}}>25~GeV$ and a photon with $p_{\mathrm{T}}>40~GeV$, with all objects within $|\eta|=2.6$.
Cutflow of expected events in the four Strong search edge signal regions. `Leptons' refers to electrons and muons only. The gluino-$Z^{(*)}$ model with $m_{ ilde{g}}=800~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRC-STR with 60000 Monte Carlo (MC) events generated. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRMed-STR with 30000 (MC) events generated. The gluino-slepton model with $m_{ ilde{g}}=2~TeV$ and $m_{ ilde{\ell}}=1.3~TeV$ is used for SRLow-STR and SRHigh-STR with 30000 MC events generated. The Generator Filter requires two 5~GeV leptons and 100~GeV of \met. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~GeV$ or at least one lepton with $p_{\mathrm{T}}>25~GeV$ and a photon with $p_{\mathrm{T}}>40~GeV$, with all objects within $|\eta|=2.6$.
Cutflow of expected events in the four Strong search edge signal regions. `Leptons' refers to electrons and muons only. The gluino-$Z^{(*)}$ model with $m_{ ilde{g}}=800~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRC-STR with 60000 Monte Carlo (MC) events generated. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRMed-STR with 30000 (MC) events generated. The gluino-slepton model with $m_{ ilde{g}}=2~TeV$ and $m_{ ilde{\ell}}=1.3~TeV$ is used for SRLow-STR and SRHigh-STR with 30000 MC events generated. The Generator Filter requires two 5~GeV leptons and 100~GeV of \met. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~GeV$ or at least one lepton with $p_{\mathrm{T}}>25~GeV$ and a photon with $p_{\mathrm{T}}>40~GeV$, with all objects within $|\eta|=2.6$.
Cutflow of expected events in the three Strong search on-$Z$ signal regions. The cutflow up to the signal region specific requirements is the same as in the Strong search edge cutflow. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for all of the on-$Z$ signal regions with 30000 (MC) events generated.
Cutflow of expected events in the three Strong search on-$Z$ signal regions. The cutflow up to the signal region specific requirements is the same as in the Strong search edge cutflow. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for all of the on-$Z$ signal regions with 30000 (MC) events generated.
Cutflow of expected events in the three Strong search on-$Z$ signal regions. The cutflow up to the signal region specific requirements is the same as in the Strong search edge cutflow. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for all of the on-$Z$ signal regions with 30000 (MC) events generated.
Table 36: Cutflow of expected events in the region SR-OffShell_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 36: Cutflow of expected events in the region SR-OffShell_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 37: Cutflow of expected events in the region SR-OffShell_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 37: Cutflow of expected events in the region SR-OffShell_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 38: Cutflow of expected events in the region SR-Low_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 38: Cutflow of expected events in the region SR-Low_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 39: Cutflow of expected events in the region SR-Low_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 39: Cutflow of expected events in the region SR-Low_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 40: Cutflow of expected events in the region SR-Low-2-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 40: Cutflow of expected events in the region SR-Low-2-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 41: Cutflow of expected events in the region SR-Int_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 41: Cutflow of expected events in the region SR-Int_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 42: Cutflow of expected events in the region SR-Int_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 42: Cutflow of expected events in the region SR-Int_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 43: Cutflow of expected events in the region SR-High_16a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 43: Cutflow of expected events in the region SR-High_16a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 44: Cutflow of expected events in the region SR-High_16b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 44: Cutflow of expected events in the region SR-High_16b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 45: Cutflow of expected events in the region SR-High_8a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 45: Cutflow of expected events in the region SR-High_8a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 46: Cutflow of expected events in the region SR-High_8b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 46: Cutflow of expected events in the region SR-High_8b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 47: Cutflow of expected events in the region SR-1J-High-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 47: Cutflow of expected events in the region SR-1J-High-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 48: Cutflow of expected events in the region SR-llbb-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 48: Cutflow of expected events in the region SR-llbb-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
The combined $m_{jj}$ distribution of CR-Z-EWK and SR-Low-EWK (left), and the $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ distribution in CR-Z-met-EWK (right), which removes the upper limit of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}}) < 9$ from the definition of CR-Z-EWK. This $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ tail overlaps other control and validation regions, but not signal regions. The arrows indicate the signal region SR-Low-EWK (left), and the $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ phase space which is not included in CR-Z-EWK (right). All EWK search control and signal regions are included in the fit. All statistical and systematic uncertainties are included in the hatched bands. The theoretical uncertainties from CR-Z-EWK are applied to CR-Z-met-EWK. The last bins contain the overflow.
The combined $m_{jj}$ distribution of CR-Z-EWK and SR-Low-EWK (left), and the $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ distribution in CR-Z-met-EWK (right), which removes the upper limit of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}}) < 9$ from the definition of CR-Z-EWK. This $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ tail overlaps other control and validation regions, but not signal regions. The arrows indicate the signal region SR-Low-EWK (left), and the $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ phase space which is not included in CR-Z-EWK (right). All EWK search control and signal regions are included in the fit. All statistical and systematic uncertainties are included in the hatched bands. The theoretical uncertainties from CR-Z-EWK are applied to CR-Z-met-EWK. The last bins contain the overflow.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Two-particle correlation measurements projected onto two-dimensional, transverse rapidity coordinates ($y_{T1},y_{T2}$), allow access to dynamical properties of the QCD medium produced in relativistic heavy-ion collisions that angular correlation measurements are not sensitive to. We report non-identified charged-particle correlations for Au + Au minimum-bias collisions at $\sqrt{s_{\rm NN}}$ = 200 GeV taken by the STAR experiment at the Relativistic Heavy-Ion Collider (RHIC). Correlations are presented as 2D functions of transverse rapidity for like-sign, unlike-sign and all charged-particle pairs, as well as for particle pairs whose relative azimuthal angles lie on the near-side, the away-side, or at all relative azimuth. The correlations are constructed using charged particles with transverse momentum $p_T \geq 0.15$ GeV/$c$, pseudorapidity from $-$1 to 1, and azimuthal angles from $-\pi$ to $\pi$. The significant correlation structures that are observed evolve smoothly with collision centrality. The major correlation features include a saddle shape plus a broad peak with maximum near $y_T \approx 3$, corresponding to $p_T \approx$ 1.5 GeV/$c$. The broad peak is observed in both like- and unlike-sign charge combinations and in near- and away-side relative azimuthal angles. The all-charge, all-azimuth correlation measurements are compared with the theoretical predictions of {\sc hijing} and {\sc epos}. The results indicate that the correlations for peripheral to mid-central collisions can be approximately described as a superposition of nucleon + nucleon collisions with minimal effects from the QCD medium. Strong medium effects are indicated in mid- to most-central collisions.
Two-dimensional correlations of charged-hadrons, all-CI, projected onto (y_t1, y_t2), in centrality bin 84-93%.
Two-dimensional correlations of charged-hadrons, all-CI, projected onto (y_t1, y_t2), in centrality bin 74-84%.
Two-dimensional correlations of charged-hadrons, all-CI, projected onto (y_t1, y_t2), in centrality bin 64-74%.
Two-dimensional correlations of charged-hadrons, all-CI, projected onto (y_t1, y_t2), in centrality bin 55-64%.
Two-dimensional correlations of charged-hadrons, all-CI, projected onto (y_t1, y_t2), in centrality bin 46-55%.
Two-dimensional correlations of charged-hadrons, all-CI, projected onto (y_t1, y_t2), in centrality bin 38-46%.
Two-dimensional correlations of charged-hadrons, all-CI, projected onto (y_t1, y_t2), in centrality bin 28-38%.
Two-dimensional correlations of charged-hadrons, all-CI, projected onto (y_t1, y_t2), in centrality bin 18-28%.
Two-dimensional correlations of charged-hadrons, all-CI, projected onto (y_t1, y_t2), in centrality bin 9-18%.
Two-dimensional correlations of charged-hadrons, all-CI, projected onto (y_t1, y_t2), in centrality bin 5-9%.
Two-dimensional correlations of charged-hadrons, all-CI, projected onto (y_t1, y_t2), in centrality bin 0-5%.
Two-dimensional correlations of charged-hadrons, all-CD, projected onto (y_t1, y_t2), in centrality bin 84-93%.
Two-dimensional correlations of charged-hadrons, all-CD, projected onto (y_t1, y_t2), in centrality bin 74-84%.
Two-dimensional correlations of charged-hadrons, all-CD, projected onto (y_t1, y_t2), in centrality bin 64-74%.
Two-dimensional correlations of charged-hadrons, all-CD, projected onto (y_t1, y_t2), in centrality bin 55-64%.
Two-dimensional correlations of charged-hadrons, all-CD, projected onto (y_t1, y_t2), in centrality bin 46-55%.
Two-dimensional correlations of charged-hadrons, all-CD, projected onto (y_t1, y_t2), in centrality bin 38-46%.
Two-dimensional correlations of charged-hadrons, all-CD, projected onto (y_t1, y_t2), in centrality bin 28-38%.
Two-dimensional correlations of charged-hadrons, all-CD, projected onto (y_t1, y_t2), in centrality bin 18-28%.
Two-dimensional correlations of charged-hadrons, all-CD, projected onto (y_t1, y_t2), in centrality bin 9-18%.
Two-dimensional correlations of charged-hadrons, all-CD, projected onto (y_t1, y_t2), in centrality bin 5-9%.
Two-dimensional correlations of charged-hadrons, all-CD, projected onto (y_t1, y_t2), in centrality bin 0-5%.
Two-dimensional correlations of charged-hadrons, all-LS, projected onto (y_t1, y_t2), in centrality bin 84-93%.
Two-dimensional correlations of charged-hadrons, all-LS, projected onto (y_t1, y_t2), in centrality bin 74-84%.
Two-dimensional correlations of charged-hadrons, all-LS, projected onto (y_t1, y_t2), in centrality bin 64-74%.
Two-dimensional correlations of charged-hadrons, all-LS, projected onto (y_t1, y_t2), in centrality bin 55-64%.
Two-dimensional correlations of charged-hadrons, all-LS, projected onto (y_t1, y_t2), in centrality bin 46-55%.
Two-dimensional correlations of charged-hadrons, all-LS, projected onto (y_t1, y_t2), in centrality bin 38-46%.
Two-dimensional correlations of charged-hadrons, all-LS, projected onto (y_t1, y_t2), in centrality bin 28-38%.
Two-dimensional correlations of charged-hadrons, all-LS, projected onto (y_t1, y_t2), in centrality bin 18-28%.
Two-dimensional correlations of charged-hadrons, all-LS, projected onto (y_t1, y_t2), in centrality bin 9-18%.
Two-dimensional correlations of charged-hadrons, all-LS, projected onto (y_t1, y_t2), in centrality bin 5-9%.
Two-dimensional correlations of charged-hadrons, all-LS, projected onto (y_t1, y_t2), in centrality bin 0-5%.
Two-dimensional correlations of charged-hadrons, all-US, projected onto (y_t1, y_t2), in centrality bin 84-93%.
Two-dimensional correlations of charged-hadrons, all-US, projected onto (y_t1, y_t2), in centrality bin 74-84%.
Two-dimensional correlations of charged-hadrons, all-US, projected onto (y_t1, y_t2), in centrality bin 64-74%.
Two-dimensional correlations of charged-hadrons, all-US, projected onto (y_t1, y_t2), in centrality bin 55-64%.
Two-dimensional correlations of charged-hadrons, all-US, projected onto (y_t1, y_t2), in centrality bin 46-55%.
Two-dimensional correlations of charged-hadrons, all-US, projected onto (y_t1, y_t2), in centrality bin 38-46%.
Two-dimensional correlations of charged-hadrons, all-US, projected onto (y_t1, y_t2), in centrality bin 28-38%.
Two-dimensional correlations of charged-hadrons, all-US, projected onto (y_t1, y_t2), in centrality bin 18-28%.
Two-dimensional correlations of charged-hadrons, all-US, projected onto (y_t1, y_t2), in centrality bin 9-18%.
Two-dimensional correlations of charged-hadrons, all-US, projected onto (y_t1, y_t2), in centrality bin 5-9%.
Two-dimensional correlations of charged-hadrons, all-US, projected onto (y_t1, y_t2), in centrality bin 0-5%.
Two-dimensional correlations of charged-hadrons, NS-LS, projected onto (y_t1, y_t2), in centrality bin 84-93%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-LS, projected onto (y_t1, y_t2), in centrality bin 74-84%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-LS, projected onto (y_t1, y_t2), in centrality bin 64-74%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-LS, projected onto (y_t1, y_t2), in centrality bin 55-64%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-LS, projected onto (y_t1, y_t2), in centrality bin 46-55%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-LS, projected onto (y_t1, y_t2), in centrality bin 38-46%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-LS, projected onto (y_t1, y_t2), in centrality bin 28-38%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-LS, projected onto (y_t1, y_t2), in centrality bin 18-28%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-LS, projected onto (y_t1, y_t2), in centrality bin 9-18%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-LS, projected onto (y_t1, y_t2), in centrality bin 5-9%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-LS, projected onto (y_t1, y_t2), in centrality bin 0-5%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-US, projected onto (y_t1, y_t2), in centrality bin 84-93%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-US, projected onto (y_t1, y_t2), in centrality bin 74-84%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-US, projected onto (y_t1, y_t2), in centrality bin 64-74%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-US, projected onto (y_t1, y_t2), in centrality bin 55-64%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-US, projected onto (y_t1, y_t2), in centrality bin 46-55%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-US, projected onto (y_t1, y_t2), in centrality bin 38-46%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-US, projected onto (y_t1, y_t2), in centrality bin 28-38%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-US, projected onto (y_t1, y_t2), in centrality bin 18-28%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-US, projected onto (y_t1, y_t2), in centrality bin 9-18%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-US, projected onto (y_t1, y_t2), in centrality bin 5-9%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-US, projected onto (y_t1, y_t2), in centrality bin 0-5%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-LS, projected onto (y_t1, y_t2), in centrality bin 84-93%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-LS, projected onto (y_t1, y_t2), in centrality bin 74-84%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-LS, projected onto (y_t1, y_t2), in centrality bin 64-74%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-LS, projected onto (y_t1, y_t2), in centrality bin 55-64%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-LS, projected onto (y_t1, y_t2), in centrality bin 46-55%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-LS, projected onto (y_t1, y_t2), in centrality bin 38-46%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-LS, projected onto (y_t1, y_t2), in centrality bin 28-38%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-LS, projected onto (y_t1, y_t2), in centrality bin 18-28%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-LS, projected onto (y_t1, y_t2), in centrality bin 9-18%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-LS, projected onto (y_t1, y_t2), in centrality bin 5-9%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-LS, projected onto (y_t1, y_t2), in centrality bin 0-5%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-US, projected onto (y_t1, y_t2), in centrality bin 84-93%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-US, projected onto (y_t1, y_t2), in centrality bin 74-84%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-US, projected onto (y_t1, y_t2), in centrality bin 64-74%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-US, projected onto (y_t1, y_t2), in centrality bin 55-64%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-US, projected onto (y_t1, y_t2), in centrality bin 46-55%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-US, projected onto (y_t1, y_t2), in centrality bin 38-46%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-US, projected onto (y_t1, y_t2), in centrality bin 28-38%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-US, projected onto (y_t1, y_t2), in centrality bin 18-28%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-US, projected onto (y_t1, y_t2), in centrality bin 9-18%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-US, projected onto (y_t1, y_t2), in centrality bin 5-9%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-US, projected onto (y_t1, y_t2), in centrality bin 0-5%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-CI, projected onto (y_t1, y_t2), in centrality bin 84-93%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-CI, projected onto (y_t1, y_t2), in centrality bin 74-84%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-CI, projected onto (y_t1, y_t2), in centrality bin 64-74%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-CI, projected onto (y_t1, y_t2), in centrality bin 55-64%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-CI, projected onto (y_t1, y_t2), in centrality bin 46-55%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-CI, projected onto (y_t1, y_t2), in centrality bin 38-46%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-CI, projected onto (y_t1, y_t2), in centrality bin 28-38%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-CI, projected onto (y_t1, y_t2), in centrality bin 18-28%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-CI, projected onto (y_t1, y_t2), in centrality bin 9-18%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-CI, projected onto (y_t1, y_t2), in centrality bin 5-9%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-CI, projected onto (y_t1, y_t2), in centrality bin 0-5%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-CI, projected onto (y_t1, y_t2), in centrality bin 84-93%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-CI, projected onto (y_t1, y_t2), in centrality bin 74-84%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-CI, projected onto (y_t1, y_t2), in centrality bin 64-74%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-CI, projected onto (y_t1, y_t2), in centrality bin 55-64%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-CI, projected onto (y_t1, y_t2), in centrality bin 46-55%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-CI, projected onto (y_t1, y_t2), in centrality bin 38-46%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-CI, projected onto (y_t1, y_t2), in centrality bin 28-38%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-CI, projected onto (y_t1, y_t2), in centrality bin 18-28%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-CI, projected onto (y_t1, y_t2), in centrality bin 9-18%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-CI, projected onto (y_t1, y_t2), in centrality bin 5-9%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-CI, projected onto (y_t1, y_t2), in centrality bin 0-5%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-CD, projected onto (y_t1, y_t2), in centrality bin 84-93%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-CD, projected onto (y_t1, y_t2), in centrality bin 74-84%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-CD, projected onto (y_t1, y_t2), in centrality bin 64-74%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-CD, projected onto (y_t1, y_t2), in centrality bin 55-64%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-CD, projected onto (y_t1, y_t2), in centrality bin 46-55%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-CD, projected onto (y_t1, y_t2), in centrality bin 38-46%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-CD, projected onto (y_t1, y_t2), in centrality bin 28-38%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-CD, projected onto (y_t1, y_t2), in centrality bin 18-28%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-CD, projected onto (y_t1, y_t2), in centrality bin 9-18%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-CD, projected onto (y_t1, y_t2), in centrality bin 5-9%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, NS-CD, projected onto (y_t1, y_t2), in centrality bin 0-5%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-CD, projected onto (y_t1, y_t2), in centrality bin 84-93%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-CD, projected onto (y_t1, y_t2), in centrality bin 74-84%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-CD, projected onto (y_t1, y_t2), in centrality bin 64-74%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-CD, projected onto (y_t1, y_t2), in centrality bin 55-64%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-CD, projected onto (y_t1, y_t2), in centrality bin 46-55%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-CD, projected onto (y_t1, y_t2), in centrality bin 38-46%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-CD, projected onto (y_t1, y_t2), in centrality bin 28-38%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-CD, projected onto (y_t1, y_t2), in centrality bin 18-28%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-CD, projected onto (y_t1, y_t2), in centrality bin 9-18%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-CD, projected onto (y_t1, y_t2), in centrality bin 5-9%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Two-dimensional correlations of charged-hadrons, AS-CD, projected onto (y_t1, y_t2), in centrality bin 0-5%. Only centrality bins 74-84%, 46 - 55%, 18-28%, and 0-5% shown in paper.
Fit results for the amplitudes of the measured and predicted correlation peak near (yT 1, yT 2) ≈ (3,3) as a function of centrality
Fit results for the yTSigma_0 of the measured and predicted correlation peak near (yT 1, yT 2) ≈ (3,3) as a function of centrality
Fit results for the amplitudes of the measured and predicted correlation peak near (yT 1, yT 2) ≈ (3,1) as a function of centrality
Fit results for the yT_1 of the measured and predicted correlation peak near (yT 1, yT 2) ≈ (3,1) as a function of centrality
Fit results for the yT_2 of the measured and predicted correlation peak near (yT 1, yT 2) ≈ (3,1) as a function of centrality
A search for a long-lived, heavy neutral lepton ($\mathcal{N}$) in 139 fb$^{-1}$ of $\sqrt{s}=13$ TeV $pp$ collision data collected by the ATLAS detector at the Large Hadron Collider is reported. The $\mathcal{N}$ is produced via $W \rightarrow \mathcal{N} \mu$ or $W \rightarrow \mathcal{N} e$ and decays into two charged leptons and a neutrino, forming a displaced vertex. The $\mathcal{N}$ mass is used to discriminate between signal and background. No signal is observed, and limits are set on the squared mixing parameters of the $\mathcal{N}$ with the left-handed neutrino states for the $\mathcal{N}$ mass range $3$ GeV $< m_{\mathcal{N}} < 15$ GeV. For the first time, limits are given for both single-flavor and multiflavor mixing scenarios motivated by neutrino flavor oscillation results for both the normal and inverted neutrino-mass hierarchies.
Expected and observed 95% CL for the 1SFH e Dirac model.
Expected and observed 95% CL for the 1SFH e Majorana model.
Expected and observed 95% CL for the 1SFH mu Dirac model.
Expected and observed 95% CL for the 1SFH mu Majorana model.
Expected and observed 95% CL for the 2QDH NH Dirac model.
Expected and observed 95% CL for the 2QDH NH Majorana model.
Expected and observed 95% CL for the 2QDH IH Dirac model.
Expected and observed 95% CL for the 2QDH IH Majorana model.
Cutflow for six simulated signal channels showing the weighted number of expected events based on the single-flavour mixing model in the Majorana limit. Each column uses the generated signal sample with the mass hypothesis $m_N = 10$ GeV and proper decay length $c\tau_N = 10$ mm.
Cutflow for the six channels in data showing the number of events passing each successive signal selection for Majorana HNLs.
The event selection efficiency for each mass-lifetime point in all six studied channels. Shown is the fraction of the produced MC simulation events that pass all signal region selections. An entry of 0 indicates no events were selected.
The dominant signal uncertainty is due to differences in reconstruction of displaced vertices and tracks between data and MC. This is evaluated by comparing $K^{0}_{S} \rightarrow \pi^+\pi^-$ event yields in the VR and in MC produced with Pythia8.186 in bins of $p_\mathrm{T}$ and $r_\mathrm{DV}$. The data/MC ratio is normalized to the bin nearest the IP where the tracking and vertexing reconstruction algorithms are expected to be most robust. The symmetrized difference from 1.0 is applied to each signal vertex as a per-event systematic variation.
Expected and observed yields in the different analysis regions (prefit) for the 1SFH e Dirac model (10 GeV, 10mm).
Expected and observed yields in the different analysis regions (postfit) for the 1SFH e Dirac model (10 GeV, 10mm).
Expected and observed yields in the different analysis regions (prefit) for the 1SFH e Majorana model (10 GeV, 10mm).
Expected and observed yields in the different analysis regions (postfit) for the 1SFH e Majorana model (10 GeV, 10mm).
Expected and observed yields in the different analysis regions (prefit) for the 1SFH u Dirac model (10 GeV, 10mm).
Expected and observed yields in the different analysis regions (postfit) for the 1SFH u Dirac model (10 GeV, 10mm).
Expected and observed yields in the different analysis regions (prefit) for the 1SFH u Majorana model (10 GeV, 10mm).
Expected and observed yields in the different analysis regions (postfit) for the 1SFH u Majorana model (10 GeV, 10mm).
Expected and observed yields in the different analysis regions (prefit) for the 2QDH (NH) Dirac model (10 GeV, 10mm).
Expected and observed yields in the different analysis regions (postfit) for the 2QDH (NH) Dirac model (10 GeV, 10mm).
Expected and observed yields in the different analysis regions (prefit) for the 2QDH (NH) Majorana model (10 GeV, 10mm).
Expected and observed yields in the different analysis regions (postfit) for the 2QDH (NH) Majorana model (10 GeV, 10mm).
Expected and observed yields in the different analysis regions (prefit) for the 2QDH (IH) Dirac model (10 GeV, 10mm).
Expected and observed yields in the different analysis regions (postfit) for the 2QDH (IH) Dirac model (10 GeV, 10mm).
Expected and observed yields in the different analysis regions (prefit) for the 2QDH (IH) Majorana model (10 GeV, 10mm).
Expected and observed yields in the different analysis regions (postfit) for the 2QDH (IH) Majorana model (10 GeV, 10mm).
The total displaced vertexing efficiency as a function of $r_{DV}$ for the custom configuration used in this analysis. The definition of the secondary vertex efficiency can be found in defined in \cite{ATL-PHYS-PUB-2019-013}. The efficiency is shown for $\mu-\mu\mu$, $\mu-\mu e$ and $\mu-ee$ signals with $m_N=10$~GeV and $c\tau_N=10$~mm.
Notwithstanding decades of progress since Yukawa first developed a description of the force between nucleons in terms of meson exchange, a full understanding of the strong interaction remains a major challenge in modern science. One remaining difficulty arises from the non-perturbative nature of the strong force, which leads to the phenomenon of quark confinement at distances on the order of the size of the proton. Here we show that in relativistic heavy-ion collisions, where quarks and gluons are set free over an extended volume, two species of produced vector (spin-1) mesons, namely $\phi$ and $K^{*0}$, emerge with a surprising pattern of global spin alignment. In particular, the global spin alignment for $\phi$ is unexpectedly large, while that for $K^{*0}$ is consistent with zero. The observed spin-alignment pattern and magnitude for the $\phi$ cannot be explained by conventional mechanisms, while a model with a connection to strong force fields, i.e. an effective proxy description within the Standard Model and Quantum Chromodynamics, accommodates the current data. This connection, if fully established, will open a potential new avenue for studying the behaviour of strong force fields.
Global spin alignment of $\phi$ and $K^{*0}$ vector mesons in heavy-ion collisions. The measured matrix element $\rho_{00}$ as a function of beam energy for the $\phi$ and $K^{*0}$ vector mesons within the indicated windows of centrality, transverse momentum ($p_T$) and rapidity ($y$). The open symbols indicate ALICE results for Pb+Pb collisions at 2.76 TeV at $p_{T}$ values of 2.0 and 1.4 GeV/c for the $\phi$ and $K^{*0}$ mesons, respectively, corresponding to the $p_{T}$ bin nearest to the mean $p_{T}$ for the 1.0 – 5.0 GeV/$c$ range assumed for each meson in the present analysis. The red solid curve is a fit to data in the range of $\sqrt{s_{NN}} = 19.6$ to 200 GeV, based on a theoretical calculation with a $\phi$-meson field. Parameter sensitivity of $\rho_{00}$ to the $\phi$-meson field is shown in Ref.5. The red dashed line is an extension of the solid curve with the fitted parameter $G_s^{(y)}$. The black dashed line represents $\rho_{00}=1/3.$
Global spin alignment of $\phi$ and $K^{*0}$ vector mesons in heavy-ion collisions. The measured matrix element $\rho_{00}$ as a function of beam energy for the $\phi$ and $K^{*0}$ vector mesons within the indicated windows of centrality, transverse momentum ($p_T$) and rapidity ($y$). The open symbols indicate ALICE results for Pb+Pb collisions at 2.76 TeV at $p_{T}$ values of 2.0 and 1.4 GeV/c for the $\phi$ and $K^{*0}$ mesons, respectively, corresponding to the $p_{T}$ bin nearest to the mean $p_{T}$ for the 1.0 – 5.0 GeV/$c$ range assumed for each meson in the present analysis. The red solid curve is a fit to data in the range of $\sqrt{s_{NN}} = 19.6$ to 200 GeV, based on a theoretical calculation with a $\phi$-meson field. Parameter sensitivity of $\rho_{00}$ to the $\phi$-meson field is shown in Ref.5. The red dashed line is an extension of the solid curve with the fitted parameter $G_s^{(y)}$. The black dashed line represents $\rho_{00}=1/3.$
Example of combinatorial background subtracted invariant mass distributions and the extracted yields as a function of $\cos \theta^*$ for $\phi$ and $K^{*0}$ mesons. \textbf{a)} example of $\phi \rightarrow K^+ + K^-$ invariant mass distributions, with combinatorial background subtracted, integrated over $\cos \theta^*$; \textbf{b)} example of $K^{*0} (\overline{K^{*0}}) \rightarrow K^{-} \pi^{+} (K^{+} \pi^{-})$ invariant mass distributions, with combinatorial background subtracted, integrated over $\cos \theta^*$; \textbf{c)} extracted yields of $\phi$ as a function of $\cos \theta^*$; \textbf{d)} extracted yields of $K^{*0}$ as a function of $\cos \theta^*$.
Example of combinatorial background subtracted invariant mass distributions and the extracted yields as a function of $\cos \theta^*$ for $\phi$ and $K^{*0}$ mesons. \textbf{a)} example of $\phi \rightarrow K^+ + K^-$ invariant mass distributions, with combinatorial background subtracted, integrated over $\cos \theta^*$; \textbf{b)} example of $K^{*0} (\overline{K^{*0}}) \rightarrow K^{-} \pi^{+} (K^{+} \pi^{-})$ invariant mass distributions, with combinatorial background subtracted, integrated over $\cos \theta^*$; \textbf{c)} extracted yields of $\phi$ as a function of $\cos \theta^*$; \textbf{d)} extracted yields of $K^{*0}$ as a function of $\cos \theta^*$.
Example of combinatorial background subtracted invariant mass distributions and the extracted yields as a function of $\cos \theta^*$ for $\phi$ and $K^{*0}$ mesons. \textbf{a)} example of $\phi \rightarrow K^+ + K^-$ invariant mass distributions, with combinatorial background subtracted, integrated over $\cos \theta^*$; \textbf{b)} example of $K^{*0} (\overline{K^{*0}}) \rightarrow K^{-} \pi^{+} (K^{+} \pi^{-})$ invariant mass distributions, with combinatorial background subtracted, integrated over $\cos \theta^*$; \textbf{c)} extracted yields of $\phi$ as a function of $\cos \theta^*$; \textbf{d)} extracted yields of $K^{*0}$ as a function of $\cos \theta^*$.
Example of combinatorial background subtracted invariant mass distributions and the extracted yields as a function of $\cos \theta^*$ for $\phi$ and $K^{*0}$ mesons. \textbf{a)} example of $\phi \rightarrow K^+ + K^-$ invariant mass distributions, with combinatorial background subtracted, integrated over $\cos \theta^*$; \textbf{b)} example of $K^{*0} (\overline{K^{*0}}) \rightarrow K^{-} \pi^{+} (K^{+} \pi^{-})$ invariant mass distributions, with combinatorial background subtracted, integrated over $\cos \theta^*$; \textbf{c)} extracted yields of $\phi$ as a function of $\cos \theta^*$; \textbf{d)} extracted yields of $K^{*0}$ as a function of $\cos \theta^*$.
Efficiency corrected $\phi$-meson yields as a function of cos$\theta$* and corresponding fits with Eq.1 in the method section.
Efficiency and acceptance corrected $K^{*0}$-meson yields as a function of cos$\theta$* and corresponding fits with Eq.4 in the method section.
$\phi$-meson $\rho_{00}$ obtained from 1st- and 2nd-order event planes. The red stars (gray squares) show the $\phi$-meson $\rho_{00}$ as a function of beam energy, obtained with the 2nd-order (1st-order) EP.
$\phi$-meson $\rho_{00}$ with respect to different quantization axes. $\phi$-meson $\rho_{00}$ as a function of beam energy, for the out-of-plane direction (stars) and the in-plane direction (diamonds). Curves are fits based on theoretical calculations with a $\phi$-meson field. The corresponding $G_s^{(y)}$ values obtained from the fits are shown in the legend.
$\rho_{00}$ as a function of transverse momentum for $\phi$ for different collision energies. The gray squares and red stars are results obtained with the 1st- and 2nd-order EP, respectively.
$\rho_{00}$ as a function of transverse momentum for $\phi$ for different collision energies. The gray squares and red stars are results obtained with the 1st- and 2nd-order EP, respectively.
$\rho_{00}$ as a function of transverse momentum for $\phi$ for different collision energies. The gray squares and red stars are results obtained with the 1st- and 2nd-order EP, respectively.
$\rho_{00}$ as a function of transverse momentum for $\phi$ for different collision energies. The gray squares and red stars are results obtained with the 1st- and 2nd-order EP, respectively.
$\rho_{00}$ as a function of transverse momentum for $\phi$ for different collision energies. The gray squares and red stars are results obtained with the 1st- and 2nd-order EP, respectively.
$\rho_{00}$ as a function of transverse momentum for $\phi$ for different collision energies. The gray squares and red stars are results obtained with the 1st- and 2nd-order EP, respectively.
$\rho_{00}$ as a function of transverse momentum for $\phi$ for different collision energies. The gray squares and red stars are results obtained with the 1st- and 2nd-order EP, respectively.
$\rho_{00}$ as a function of transverse momentum for $\phi$ for different collision energies. The gray squares and red stars are results obtained with the 1st- and 2nd-order EP, respectively.
$\rho_{00}$ as a function of transverse momentum for $\phi$ for different collision energies. The gray squares and red stars are results obtained with the 1st- and 2nd-order EP, respectively.
$\rho_{00}$ as a function of transverse momentum for $\phi$ for different collision energies. The gray squares and red stars are results obtained with the 1st- and 2nd-order EP, respectively.
$\rho_{00}$ as a function of transverse momentum for $\phi$ for different collision energies. The gray squares and red stars are results obtained with the 1st- and 2nd-order EP, respectively.
$\rho_{00}$ as a function of transverse momentum for $\phi$ for different collision energies. The gray squares and red stars are results obtained with the 1st- and 2nd-order EP, respectively.
$\rho_{00}$ as a function of transverse momentum for $K^{*0}$ for different collision energies.
$\rho_{00}$ as a function of transverse momentum for $K^{*0}$ for different collision energies.
$\rho_{00}$ as a function of transverse momentum for $K^{*0}$ for different collision energies.
$\rho_{00}$ as a function of transverse momentum for $K^{*0}$ for different collision energies.
$\rho_{00}$ as a function of transverse momentum for $K^{*0}$ for different collision energies.
$\rho_{00}$ as a function of transverse momentum for $K^{*0}$ for different collision energies.
$\rho_{00}$ as a function of transverse momentum for $K^{*0}$ for different collision energies.
$\rho_{00}$ as a function of centrality for $\phi$ (upper panels) and $K^{*0}$ (lower panels). The solid squares and stars are results for the $\phi$ meson, obtained with the 1st- and 2nd-order EP, respectively. The solid circles are results for the $K^{*0}$ meson, obtained with the 2nd-order EP.}
$\rho_{00}$ as a function of centrality for $\phi$ (upper panels) and $K^{*0}$ (lower panels). The solid squares and stars are results for the $\phi$ meson, obtained with the 1st- and 2nd-order EP, respectively. The solid circles are results for the $K^{*0}$ meson, obtained with the 2nd-order EP.}
$\rho_{00}$ as a function of centrality for $\phi$ (upper panels) and $K^{*0}$ (lower panels). The solid squares and stars are results for the $\phi$ meson, obtained with the 1st- and 2nd-order EP, respectively. The solid circles are results for the $K^{*0}$ meson, obtained with the 2nd-order EP.}
$\rho_{00}$ as a function of centrality for $\phi$ (upper panels) and $K^{*0}$ (lower panels). The solid squares and stars are results for the $\phi$ meson, obtained with the 1st- and 2nd-order EP, respectively. The solid circles are results for the $K^{*0}$ meson, obtained with the 2nd-order EP.}
$\rho_{00}$ as a function of centrality for $\phi$ (upper panels) and $K^{*0}$ (lower panels). The solid squares and stars are results for the $\phi$ meson, obtained with the 1st- and 2nd-order EP, respectively. The solid circles are results for the $K^{*0}$ meson, obtained with the 2nd-order EP.}
$\rho_{00}$ as a function of centrality for $\phi$ (upper panels) and $K^{*0}$ (lower panels). The solid squares and stars are results for the $\phi$ meson, obtained with the 1st- and 2nd-order EP, respectively. The solid circles are results for the $K^{*0}$ meson, obtained with the 2nd-order EP.}
$\rho_{00}$ as a function of centrality for $\phi$ (upper panels) and $K^{*0}$ (lower panels). The solid squares and stars are results for the $\phi$ meson, obtained with the 1st- and 2nd-order EP, respectively. The solid circles are results for the $K^{*0}$ meson, obtained with the 2nd-order EP.}
Global spin alignment measurement of $\phi$ and $K^{*0}$ vector mesons in Au+Au collisions at 0-20\% centrality. The solid squares and stars are results for the $\phi$ meson, obtained with the 1st- and 2nd-order EP, respectively. The solid circles are results for $K^{*0}$-meson, obtained with the 2nd-order EP.
Global spin alignment measurement of $\phi$ and $K^{*0}$ vector mesons in Au+Au collisions at 0-20\% centrality. The solid squares and stars are results for the $\phi$ meson, obtained with the 1st- and 2nd-order EP, respectively. The solid circles are results for $K^{*0}$-meson, obtained with the 2nd-order EP.
A linearly polarized photon can be quantized from the Lorentz-boosted electromagnetic field of a nucleus traveling at ultra-relativistic speed. When two relativistic heavy nuclei pass one another at a distance of a few nuclear radii, the photon from one nucleus may interact through a virtual quark-antiquark pair with gluons from the other nucleus forming a short-lived vector meson (e.g. ${\rho^0}$). In this experiment, the polarization was utilized in diffractive photoproduction to observe a unique spin interference pattern in the angular distribution of ${\rho^0\rightarrow\pi^+\pi^-}$ decays. The observed interference is a result of an overlap of two wave functions at a distance an order of magnitude larger than the ${\rho^0}$ travel distance within its lifetime. The strong-interaction nuclear radii were extracted from these diffractive interactions, and found to be $6.53\pm 0.06$ fm ($^{197} {\rm Au }$) and $7.29\pm 0.08$ fm ($^{238} {\rm U}$), larger than the nuclear charge radii. The observable is demonstrated to be sensitive to the nuclear geometry and quantum interference of non-identical particles.
We present high-precision measurements of elliptic, triangular, and quadrangular flow $v_{2}$, $v_{3}$, and $v_{4}$, respectively, at midrapidity ($|\eta|<1.0$) for identified hadrons $\pi$, $p$, $K$, $\varphi$, $K_s$, $\Lambda$ as a function of centrality and transverse momentum in Au+Au collisions at the center-of-mass energy $\sqrt{s_{\rm NN}}=$ 200 GeV. We observe similar $v_{n}$ trends between light and strange mesons which indicates that the heavier strange quarks flow as strongly as the lighter up and down quarks. The number-of-constituent-quark scaling for $v_{2}$, $v_{3}$, and $v_{4}$ is found to hold within statistical uncertainty for 0-10$\%$, 10-40$\%$ and 40-80$\%$ collision centrality intervals. The results are compared to several viscous hydrodynamic calculations with varying initial conditions, and could serve as an additional constraint to the development of hydrodynamic models.
The transverse momentum dependence of elliptic, triangular and quadrangular flow of particles, antiparticles and their difference for 0-80 central Au+Au collisions.
The transverse momentum dependence of elliptic, triangular and quadrangular flow of particles, antiparticles and their difference for 0-80 central Au+Au collisions.
The transverse momentum dependence of elliptic, triangular and quadrangular flow of particles, antiparticles and their difference for 0-80 central Au+Au collisions.
The transverse momentum dependence of elliptic, triangular and quadrangular flow of particles, antiparticles and their difference for 0-80 central Au+Au collisions.
The transverse momentum dependence of elliptic, triangular and quadrangular flow of particles, antiparticles and their difference for 0-80 central Au+Au collisions.
The transverse momentum dependence of elliptic, triangular and quadrangular flow of particles, antiparticles and their difference for 0-80 central Au+Au collisions.
The transverse momentum dependence of elliptic flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of elliptic flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of elliptic flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of elliptic flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of elliptic flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of elliptic flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of triangular flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of triangular flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of triangular flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of triangular flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of triangular flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of triangular flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of triangular flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of triangular flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of triangular flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of triangular flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of elliptic, triangular and quadrangular flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of elliptic, triangular and quadrangular flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of quadrangular flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of quadrangular flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of quadrangular flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of quadrangular flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of quadrangular flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of quadrangular flow of particles, antiparticles for 0-80 central Au+Au collisions.
The transverse momentum dependence of $v_{2} \pi^{+}$ for 0-10 central Au+Au collisions.
The transverse momentum dependence of $v_{2} \pi^{+}$ for 0-10 central Au+Au collisions.
The transverse momentum dependence of $v_{2} \pi^{+}$ for 10-40 central Au+Au collisions.
The transverse momentum dependence of $v_{2} \pi^{+}$ for 10-40 central Au+Au collisions.
The transverse momentum dependence of $v_{2} \pi^{+}$, for 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{2} \pi^{+}$, for 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{2} k^{+}$ for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{2} k^{+}$ for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{2} p^{+}$, for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{2} p^{+}$, for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{3} \pi^{+}$ for 0-10 central Au+Au collisions.
The transverse momentum dependence of $v_{3} \pi^{+}$ for 0-10 central Au+Au collisions.
The transverse momentum dependence of $v_{3} \pi^{+}$ for 10-40 central Au+Au collisions.
The transverse momentum dependence of $v_{3} \pi^{+}$ for 10-40 central Au+Au collisions.
The transverse momentum dependence of $v_{3} \pi^{+}$, for 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{3} \pi^{+}$, for 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{3} k^{+}$ for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{3} k^{+}$ for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{3} p^{+}$, for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{3} p^{+}$, for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{4} \pi^{+}$ for 0-10 central Au+Au collisions.
The transverse momentum dependence of $v_{4} \pi^{+}$ for 0-10 central Au+Au collisions.
The transverse momentum dependence of $v_{4} \pi^{+}$ for 10-40 central Au+Au collisions.
The transverse momentum dependence of $v_{4} \pi^{+}$ for 10-40 central Au+Au collisions.
The transverse momentum dependence of $v_{4} \pi^{+}$, for 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{4} \pi^{+}$, for 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{4} K^{+}$ for 0-10 central Au+Au collisions.
The transverse momentum dependence of $v_{4} K^{+}$ for 0-10 central Au+Au collisions.
The transverse momentum dependence of $v_{4} K^{+}$ for 10-40 central Au+Au collisions.
The transverse momentum dependence of $v_{4} K^{+}$ for 10-40 central Au+Au collisions.
The transverse momentum dependence of $v_{4} K^{+}$, for 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{4} K^{+}$, for 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{4} p^{+}$ for 0-10 central Au+Au collisions.
The transverse momentum dependence of $v_{4} p^{+}$ for 0-10 central Au+Au collisions.
The transverse momentum dependence of $v_{4} p^{+}$ for 10-40 central Au+Au collisions.
The transverse momentum dependence of $v_{4} p^{+}$ for 10-40 central Au+Au collisions.
The transverse momentum dependence of $v_{4} p^{+}$, for 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{4} p^{+}$, for 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{2} \pi^{-}$ for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{2} \pi^{-}$ for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{2} k^{-}$ for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{2} k^{-}$ for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{2} p^{-}$ for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{2} p^{-}$ for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{3} \pi^{-}$ for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{3} \pi^{-}$ for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{3}ki^{-}$ for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{3}ki^{-}$ for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{3} p^{-}$ for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{3} p^{-}$ for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{4} \pi^{-}$ for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{4} \pi^{-}$ for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{4} p^{-}$ for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{4} p^{-}$ for 0-10, 10-40 and 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{4} k^{-}$ for 0-10 central Au+Au collisions.
The transverse momentum dependence of $v_{4} k^{-}$ for 0-10 central Au+Au collisions.
The transverse momentum dependence of $v_{4} k^{-}$ for 10-40 central Au+Au collisions.
The transverse momentum dependence of $v_{4} k^{-}$ for 10-40 central Au+Au collisions.
The transverse momentum dependence of $v_{4} k^{-}$ for 40-80 central Au+Au collisions.
The transverse momentum dependence of $v_{4} k^{-}$ for 40-80 central Au+Au collisions.
The centrality dependence of $v_{2} (\pi, K, p) $ for Au+Au collisions.
The centrality dependence of $v_{2} (\pi, K, p) $ for Au+Au collisions.
The centrality dependence of $v_{3} (\pi, K, p) $ for Au+Au collisions.
The centrality dependence of $v_{3} (\pi, K, p) $ for Au+Au collisions.
A study of $B_c^+\to J/\psi D_s^+$ and $B_c^+\to J/\psi D_s^{*+}$ decays using 139 fb$^{-1}$ of integrated luminosity collected with the ATLAS detector from $\sqrt{s} = 13$ TeV $pp$ collisions at the LHC is presented. The ratios of the branching fractions of the two decays to the branching fraction of the $B_c^+\to J/\psi \pi^+$ decay are measured: $\mathcal B(B_c^+\to J/\psi D_s^+)/\mathcal B(B_c^+\to J/\psi \pi^+) = 2.76\pm 0.47$ and $\mathcal B(B_c^+\to J/\psi D_s^{*+})/\mathcal B(B_c^+\to J/\psi \pi^+) = 5.33\pm 0.96$. The ratio of the branching fractions of the two decays is found to be $\mathcal B(B_c^+\to J/\psi D_s^{*+})/\mathcal B(B_c^+\to J/\psi D_s^+) = 1.93\pm0.26$. For the $B_c^+\to J/\psi D_s^{*+}$ decay, the transverse polarization fraction, $\Gamma_{\pm\pm}/\Gamma$, is measured to be $0.70\pm0.11$. The reported uncertainties include both the statistical and systematic components added in quadrature. The precision of the measurements exceeds that in all previous studies of these decays. These results supersede those obtained in the earlier ATLAS study of the same decays with $\sqrt{s} = 7$ and 8 TeV $pp$ collision data. A comparison with available theoretical predictions for the measured quantities is presented.
Measured values of $R_{D_s^+/\pi^+}$, $R_{D_s^{*+}/\pi^+}$, $R_{D_s^{*+}/D_s^+}$ ratios of branching fractions, fraction of transverse polarization $\Gamma_{\pm\pm}/\Gamma$ with their statistical uncertainties and full breakdown of systematic uncertainties. Predictions of various theory calculations are also shown with their uncertainties where available, as well as the estimates based on similar decays of light $B$ mesons.
Parameters of the $B_c^+\to J/\psi D_s^+$ and $B_c^+\to J/\psi D_s^{*+}$ signals obtained with the unbinned extended maximum-likelihood fit to the data. Only the statistical uncertainties are included. No acceptance or efficiency corrections are applied to the signal yields.
Parameters of the $B_c^+\to J/\psi \pi^+$ signal obtained with the unbinned extended maximum-likelihood fit. Only the statistical uncertainties are included. No efficiency correction is applied to the signal yield.
Summary of the total efficiencies. Quoted uncertainties correspond to statistical uncertainties of the simulated samples used. For the $B_c^+\to J/\psi \pi^+$ channel, the efficiency $\epsilon_{B_c^+\to J/\psi \pi^+}$ entering the equations for $R_{D_s^{(*)+}/\pi^+}$ is shown, while the efficiency for the full dataset is not defined.
A search for decays of pair-produced neutral long-lived particles (LLPs) is presented using 139 fb$^{-1}$ of proton-proton collision data collected by the ATLAS detector at the LHC in 2015-2018 at a centre-of-mass energy of 13 TeV. Dedicated techniques were developed for the reconstruction of displaced jets produced by LLPs decaying hadronically in the ATLAS hadronic calorimeter. Two search regions are defined for different LLP kinematic regimes. The observed numbers of events are consistent with the expected background, and limits for several benchmark signals are determined. For a SM Higgs boson with a mass of 125 GeV, branching ratios above 10% are excluded at 95% confidence level for values of $c$ times LLP mean proper lifetime in the range between 20 mm and 10 m depending on the model. Upper limits are also set on the cross-section times branching ratio for scalars with a mass of 60 GeV and for masses between 200 GeV and 1 TeV.
CalRatio triggers which were available during the LHC Run 2 data-taking, and corresponding integrated luminosity collected in each period. The high-E<sub>T</sub> CalRatio trigger with E<sub>T</sub> > 60 GeV was disabled in 2017 for instantaneous luminosities higher than 1.4 × 10<sup>34</sup> cm<sup>-2</sup> s<sup>-1</sup>. Two versions of the low-E<sub>T</sub> CalRatio trigger were used, with slight differences in their algorithms. The details are reported in Section 4.
Trigger efficiency for simulated signal events as a function of the LLP p<sub>T</sub> for one of the low-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the high-E<sub>T</sub> L1 triggers with E<sub>T</sub> thresholds of 60 GeV and 100 GeV and by the two versions of the low-E<sub>T</sub> L1 triggers. Only statistical uncertainties are shown.
Trigger efficiency for simulated signal events as a function of the LLP p<sub>T</sub> for one of the high-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the high-E<sub>T</sub> L1 triggers with E<sub>T</sub> thresholds of 60 GeV and 100 GeV and by the two versions of the low-E<sub>T</sub> L1 triggers. Only statistical uncertainties are shown.
Trigger efficiency for simulated signal events as a function of the LLP decay position in the x–y plane (L<sub>xy</sub>) for LLPs decaying in the barrel (|η|<1.4) for one of the low-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the high-E<sub>T</sub> L1 triggers with E<sub>T</sub> thresholds of 60 GeV and 100 GeV and by the two versions of the low-E<sub>T</sub> L1 triggers. Only statistical uncertainties are shown.
Trigger efficiency for simulated signal events as a function of the LLP decay position in the x–y plane (L<sub>xy</sub>) for LLPs decaying in the barrel (|η|<1.4) for one of the high-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the high-E<sub>T</sub> L1 triggers with E<sub>T</sub> thresholds of 60 GeV and 100 GeV and by the two versions of the low-E<sub>T</sub> L1 triggers. Only statistical uncertainties are shown.
The NN output scores in the dijet control region for the low-E<sub>T</sub> training with no adversary network. Statistical uncertainties are shown in all plots. In cases where training with adversary networks is considered, the systematic uncertainty related to modelling discrepancies is included as well.
The NN output scores in the dijet control region for the low-E<sub>T</sub> training with an adversary network included. Statistical uncertainties are shown in all plots. In cases where training with adversary networks is considered, the systematic uncertainty related to modelling discrepancies is included as well.
The NN output scores in the dijet control region for the high-E<sub>T</sub> training with no adversary network. Statistical uncertainties are shown in all plots. In cases where training with adversary networks is considered, the systematic uncertainty related to modelling discrepancies is included as well.
The NN output scores in the dijet control region for the high-E<sub>T</sub> training with an adversary network included. Statistical uncertainties are shown in all plots. In cases where training with adversary networks is considered, the systematic uncertainty related to modelling discrepancies is included as well.
Distribution of the low-E<sub>T</sub> per-event BDT in main data, BIB data and some of the benchmark signal samples after preselection. Only statistical uncertainties are shown.
Distribution of the high-E<sub>T</sub> per-event BDT outputs in main data, BIB data and some of the benchmark signal samples after preselection. Only statistical uncertainties are shown.
Sequential impact of each requirement on the number of events passing the selection for the high-E<sub>T</sub> selections. The signal columns represent the cumulative fraction of events passing the selection than the number of events.
Sequential impact of each requirement on the number of events passing the selection for the low-E<sub>T</sub> selections. The signal columns represent the cumulative fraction of events passing the selection than the number of events.
Application of the modified ABCD method to the final high-E<sub>T</sub> selections. The a priori estimate refers to the "pre-unblinding" case, where the data in region A are ignored by removing the Poisson constraint in that region and the signal strength is fixed to zero. This matches the simple N<sup>bkg</sup><sub>A</sub>=(N<sup>bkg</sup><sub>B</sub>· N<sup>bkg</sup><sub>C</sub>)/N<sup>bkg</sup><sub>D</sub> relation. The a posteriori estimate refers to the "post-unblinding" case, including the observed data in region A in the background-only global fit, obtained by fixing the signal strength to 0 (background-only fit) or allowing it to float (signal-plus-background fit). The table also shows one set of representative signal yields in each selection for the signal-plus-background fit. Only statistical uncertainties are included in the quoted error of the background, while the uncertainties in the signal include those from both statistical and experimental sources.
Application of the modified ABCD method to the final low-E<sub>T</sub> selections. The a priori estimate refers to the "pre-unblinding" case, where the data in region A are ignored by removing the Poisson constraint in that region and the signal strength is fixed to zero. This matches the simple N<sup>bkg</sup><sub>A</sub>=(N<sup>bkg</sup><sub>B</sub>· N<sup>bkg</sup><sub>C</sub>)/N<sup>bkg</sup><sub>D</sub> relation. The a posteriori estimate refers to the "post-unblinding" case, including the observed data in region A in the background-only global fit, obtained by fixing the signal strength to 0 (background-only fit) or allowing it to float (signal-plus-background fit). The table also shows one set of representative signal yields in each selection for the signal-plus-background fit. Only statistical uncertainties are included in the quoted error of the background, while the uncertainties in the signal include those from both statistical and experimental sources.
95% CL expected and observed limits on the BR of SM Higgs bosons to pairs of neutral LLPs (B<sub>H→ ss</sub>), showing the ± 1 σ (green) and ± 2 σ (yellow) expected limit bands, as well as a comparison with the results from previous ATLAS searches [36,78]. The cross-section for SM Higgs boson gluon--gluon fusion production is assumed to be 48.6 pb.
Trigger efficiency of simulated signal events as a function of the LLP decay position in the z direction for LLPs decaying in the calorimeter endcaps (1.4 ≤ |η| < 2.5) for one of the low-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the 60 GeV-high-E<sub>T</sub> trigger and by the two versions of the low-E<sub>T</sub> triggers.
Trigger efficiency of simulated signal events as a function of the LLP decay position in the z direction for LLPs decaying in the calorimeter endcaps (1.4 ≤ |η| < 2.5) for one of the high-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the 60 GeV-high-E<sub>T</sub> trigger and by the two versions of the low-E<sub>T</sub> triggers.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The event BDT in the dijet control region for the low-E<sub>T</sub> training training. The uncertainties are combined statistical and ML modelling systematic uncertainty.
The event BDT in the dijet control region for the high-E<sub>T</sub> training. The uncertainties are combined statistical and ML modelling systematic uncertainty.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 60 GeV compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 60 GeV compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass 125 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass 125 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available. The 125 GeV mediator is assumed to be the SM Higgs boson.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass 125 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available. The 125 GeV mediator is assumed to be the SM Higgs boson.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 200 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 400 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass 600 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 600 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ masses of of 1000 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ masses of of 1000 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ masses of of 1000 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ masses of of 1000 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
Efficiency for an event to enter Region A of the high-E<sub>T</sub> selection, as a function of the two LLP kinematics, decay type and decay position. LLP p<sub>T</sub> is binned in the ranges of [0, 50 , 100, 200 , 400, 1600] GeV (5 bins), the LLP decay position is binned in decay position in L<sub>xy</sub> in [0, 1.5, 2, 2.5, 3, 3.5, 3.9, ∞] m for LLPs with |η| < 1.5 and L<sub>z</sub> in [0, 3.6, 4.2, 4.8, 5.5, 6, ∞ ] m for LLPs with |η|geq1.5, 13 bins in all. Finally, four decay types are considered: LLPs decaying to pairs of c, b, t, or τ in bins 0, 1, 2, 3 respectively. The efficiency is presented as a function of "Bin Index", which is calculated as follows: Bin Index = (decay position bin index ) × (number of p<sub>T</sub> bins × number of decay type bins) + p<sub>T</sub> bin index * (number of decay type bin) + decay type bin index. The efficiency for a given pair-produced LLP sample can be obtained by summing the efficiency values for each event as extracted from this map, and dividing it by the total number of events in the sample. The efficiency map is symmetric between the LLPs, so the choice of LLP1 and LLP2 is arbitrary. For the high-E<sub>T</sub> selections, for overall efficiencies above 0.5%, the results are typically accurate to around 25%, but below this the efficiency can be overestimated and therefore should not be used for re-interpration.
Efficiency for an event to enter Region A of the low-E<sub>T</sub> selection, as a function of the two LLP kinematics, decay type and decay position. LLP p<sub>T</sub> is binned in the ranges of [0, 50 , 100, 200 , 400, 1600] GeV (5 bins), the LLP decay position is binned in L<sub>xy</sub> in [0, 1.5, 2, 2.5, 3, 3.5, 3.9, ∞] m for LLPs with |η| < 1.5 and L<sub>z</sub> in [0, 3.6, 4.2, 4.8, 5.5, 6, ∞ ] m for LLPs with |η|geq1.5, 13 bins in all. Finally, four decay types are considered: LLPs decaying to pairs of c, b, t, or τ in bins 0, 1, 2, 3 respectively. The efficiency is presented as a function of "Bin Index", which is calculated as follows: Bin Index = (decay position bin index ) × (number of p<sub>T</sub> bins × number of decay type bins) + p<sub>T</sub> bin index * (number of decay type bin) + decay type bin index. The efficiency for a given pair-produced LLP sample can be obtained by summing the efficiency values for each event as extracted from this map, and dividing it by the total number of events in the sample. The efficiency map is symmetric between the LLPs, so the choice of LLP1 and LLP2 is arbitrary. For the low-E<sub>T</sub> selections, for overall efficiencies above 0.15%, the results are typically accurate to around 33%, and below this the efficiency is typically accurate up to a factor of 3.
A search for events with two displaced vertices from long-lived particles (LLP) pairs using data collected by the ATLAS detector at the LHC is presented. This analysis uses 139~fb$^{-1}$ of proton-proton collision data at $\sqrt{s}=13$ TeV recorded in 2015-2018. The search employs techniques for reconstructing vertices of LLPs decaying to jets in the muon spectrometer displaced between 3 m and 14 m with respect to the primary interaction vertex. The observed numbers of events are consistent with the expected background and limits for several benchmark signals are determined. For the Higgs boson with a mass of 125 GeV, the paper reports the first exclusion limits for branching fractions into neutral long-lived particles below 0.1%, while branching fractions above 10% are excluded at 95% confidence level for LLP proper lifetimes ranging from 4 cm to 72.4 m. In addition, the paper present the first results for the decay of LLPs into into $t\bar{t}$ in the ATLAS muon spectrometer.
Efficiency for the Muon RoI Cluster trigger as a function of the decay position of the LLP for some scalar portal samples in the MS barrel for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, “RPC 1/2” represent the first/second stations of RPC chambers, “TGC 1” represents the first stations of TGC chambers and “L/S” indicate whether they are in the Large or Small sectors.
Efficiency for the Muon RoI Cluster trigger as a function of the decay position of the LLP for some scalar portal samples in the MS endcaps for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, “RPC 1/2” represent the first/second stations of RPC chambers, “TGC 1” represents the first stations of TGC chambers and “L/S” indicate whether they are in the Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS barrel fiducial volume as a function of the transverse decay position of the LLP for scalar portal samples with $m_\varPhi=125$~\GeV\ for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where ``HCal end'' is the outer limit of the hadronic calorimeter, ``MDT 1/2'' represent the first/second stations of MDT chambers and ``L/S'' indicate whether they are in Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS endcaps fiducial volume as a function of the longitudinal decay position of the LLP for scalar portal samples with $m_\varPhi=125$~\GeV\ for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where ``HCal end'' is the outer limit of the hadronic calorimeter, ``MDT 1/2'' represent the first/second stations of MDT chambers and ``L/S'' indicate whether they are in Large or Small sectors.
Expected and observed 95% CL limits on ($\sigma / \sigma_{\text{SM}}) \times B$ for $m_\phi=125$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Efficiency for the Muon RoI Cluster trigger in the MS barrel fiducial volume as a function of the transverse decay position of the LLP for non-SM Higgs benchmark samples for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling. The vertical lines show the relevant detector boundaries, where ``HCal end'' is the outer limit of the hadronic calorimeter, RPC 1/2 represent the first/second stations of RPC chambers, TGC 1 represents the first stations of TGC chambers and L/S indicate whether they are in the Large or Small sectors. The dependence on detector geometry can be seen by looking at the various detector boundaries depicted by dashed lines in the plots. In the barrel the trigger efficiency increases when the LLP decays are close to the end of the hadronic calorimeter (r ~ 4 m) and substantially decreases as the decay occurs closer to the middle station of the muon spectrometer (r ~ 7 m). For decays occurring close to the middle station the charged hadrons and photons (and their EM showers) are not spatially separated, resulting in low efficiency (the same reasoning is applicable to decays in the endcap regions).
Efficiency for the Muon RoI Cluster trigger in the MS endcaps fiducial volume as a function of the longitudinal decay position of the LLP for non-SM Higgs benchmark samples for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling. The vertical lines show the relevant detector boundaries, where ``HCal end'' is the outer limit of the hadronic calorimeter, RPC 1/2 represent the first/second stations of RPC chambers, TGC 1 represents the first stations of TGC chambers and L/S indicate whether they are in the Large or Small sectors. The dependence on detector geometry can be seen by looking at the various detector boundaries depicted by dashed lines in the plots. In the barrel the trigger efficiency increases when the LLP decays are close to the end of the hadronic calorimeter (r ~ 4 m) and substantially decreases as the decay occurs closer to the middle station of the muon spectrometer (r ~ 7 m). For decays occurring close to the middle station the charged hadrons and photons (and their EM showers) are not spatially separated, resulting in low efficiency (the same reasoning is applicable to decays in the endcap regions).
Efficiency for the Muon RoI Cluster trigger in the MS barrel fiducial volume as a function of the transverse decay position of the LLP for non-SM Higgs benchmark samples for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling. The vertical lines show the relevant detector boundaries, where ``HCal end'' is the outer limit of the hadronic calorimeter, RPC 1/2 represent the first/second stations of RPC chambers, TGC 1 represents the first stations of TGC chambers and L/S indicate whether they are in the Large or Small sectors. The dependence on detector geometry can be seen by looking at the various detector boundaries depicted by dashed lines in the plots. In the barrel the trigger efficiency increases when the LLP decays are close to the end of the hadronic calorimeter (r ~ 4 m) and substantially decreases as the decay occurs closer to the middle station of the muon spectrometer (r ~ 7 m). For decays occurring close to the middle station the charged hadrons and photons (and their EM showers) are not spatially separated, resulting in low efficiency (the same reasoning is applicable to decays in the endcap regions).
Efficiency for the Muon RoI Cluster trigger in the MS endcaps fiducial volume as a function of the longitudinal decay position of the LLP for non-SM Higgs benchmark samples for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling. The vertical lines show the relevant detector boundaries, where ``HCal end'' is the outer limit of the hadronic calorimeter, RPC 1/2 represent the first/second stations of RPC chambers, TGC 1 represents the first stations of TGC chambers and L/S indicate whether they are in the Large or Small sectors. The dependence on detector geometry can be seen by looking at the various detector boundaries depicted by dashed lines in the plots. In the barrel the trigger efficiency increases when the LLP decays are close to the end of the hadronic calorimeter (r ~ 4 m) and substantially decreases as the decay occurs closer to the middle station of the muon spectrometer (r ~ 7 m). For decays occurring close to the middle station the charged hadrons and photons (and their EM showers) are not spatially separated, resulting in low efficiency (the same reasoning is applicable to decays in the endcap regions).
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency to reconstruct an MS DV in the MS barrel fiducial volume as a function of transverse decay position of the LLP for non-SM Higgs benchmark samples for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS endcaps fiducial volume as a function of longitudinal decay position of the LLP for non-SM Higgs benchmark samples for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS barrel fiducial volume as a function of transverse decay position of the LLP for non-SM Higgs benchmark samples for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS endcaps fiducial volume as a function of longitudinal decay position of the LLP for non-SM Higgs benchmark samples for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS barrel fiducial volume as a function of the transverse decay position of the LLP for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS endcaps fiducial volume as a function of the longitudinal decay position of the LLP for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS barrel fiducial volume as a function of the transverse decay position of the LLP for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS endcaps fiducial volume as a function of the longitudinal decay position of the LLP for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS barrel fiducial volume as a function of the transverse decay position of the LLP for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS endcaps fiducial volume as a function of the longitudinal decay position of the LLP for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Expected and observed 95% CL limits on ($\sigma / \sigma_{\text{SM}}) \times B$ for $m_\phi=125$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on ($\sigma / \sigma_{\text{SM}}) \times B$ for $m_\phi=125$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on ($\sigma / \sigma_{\text{SM}}) \times B$ for $m_\phi=125$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=60$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=60$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=200$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=400$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=600$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=600$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=600$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=1000$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=1000$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=1000$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
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