A measurement of jet substructure variables is presented using data collected in 2016 by the ATLAS experiment at the LHC with proton-proton collisions at $\sqrt{s}=13$ TeV. Large-radius jets groomed with the trimming and soft-drop algorithms are studied. Dedicated event selections are used to study jets produced by light quarks or gluons, and hadronically decaying top quarks and $W$ bosons. The variables measured are sensitive to pronged substructure, and therefore are typically used for tagging jets from boosted massive particles. These include the energy correlation functions and the $N$-subjettiness variables. The number of subjets and the Les Houches angularity are also considered. The distributions of the substructure variables, corrected for detector effects, are compared to the predictions of various Monte Carlo event generators. They are also compared between the large-radius jets originating from light quarks or gluons, and hadronically decaying top quarks and $W$ bosons.
Figure 3a, Normalised differential Nsubjets distribution for soft-drop groomed jets, Dijet selection.
Figure 4a, Normalised differential LHA distribution for soft-drop groomed jets, Dijet selection
Figure 5a, Normalised differential C2 distribution for soft-drop groomed jets, Dijet selection
Jet substructure observables have significantly extended the search program for physics beyond the Standard Model at the Large Hadron Collider. The state-of-the-art tools have been motivated by theoretical calculations, but there has never been a direct comparison between data and calculations of jet substructure observables that are accurate beyond leading-logarithm approximation. Such observables are significant not only for probing the collinear regime of QCD that is largely unexplored at a hadron collider, but also for improving the understanding of jet substructure properties that are used in many studies at the Large Hadron Collider. This Letter documents a measurement of the first jet substructure quantity at a hadron collider to be calculated at next-to-next-to-leading-logarithm accuracy. The normalized, differential cross-section is measured as a function of log$_{10}\rho^2$, where $\rho$ is the ratio of the soft-drop mass to the ungroomed jet transverse momentum. This quantity is measured in dijet events from 32.9 fb$^{-1}$ of $\sqrt{s} = 13$ TeV proton-proton collisions recorded by the ATLAS detector. The data are unfolded to correct for detector effects and compared to precise QCD calculations and leading-logarithm particle-level Monte Carlo simulations.
Data from Fig 3a. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 3b. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 3c. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. The uncertainties are applied symmetrically, though the cross section cannot go below zero in the first bin.
Forum