This Letter presents the first measurements of the groomed jet radius $R_\mathrm{g}$ and the jet girth $g$ in events with an isolated photon recoiling against a jet in lead-lead (PbPb) and proton-proton (pp) collisions at the LHC at a nucleon-nucleon center-of-mass energy of 5.02 TeV. The observables $R_\mathrm{g}$ and $g$ provide a quantitative measure of how narrow or broad a jet is. The analysis uses PbPb and pp data samples with integrated luminosities of 1.7 nb$^{-1}$ and 301 pb$^{-1}$, respectively, collected with the CMS experiment in 2018 and 2017. Events are required to have a photon with transverse momentum $p_\mathrm{T}^\gamma$$>$ 100 GeV and at least one jet back-to-back in azimuth with respect to the photon and with transverse momentum $p_\mathrm{T}^\text{jet}$ such that $p_\mathrm{T}^\text{jet} / p_\mathrm{T}^\gamma$$>$ 0.4. The measured $R_\mathrm{g}$ and $g$ distributions are unfolded to the particle level, which facilitates the comparison between the PbPb and pp results and with theoretical predictions. It is found that jets with $p_\mathrm{T}^\text{jet} / p_\mathrm{T}^\gamma$$>$ 0.8, i.e., those that closely balance the photon $p_\mathrm{T}^\gamma$, are narrower in PbPb than in pp collisions. Relaxing the selection to include jets with $p_\mathrm{T}^\text{jet} / p_\mathrm{T}^\gamma$$>$ 0.4 reduces the narrowing of the angular structure of jets in PbPb relative to the pp reference. This shows that selection bias effects associated with jet energy loss play an important role in the interpretation of jet substructure measurements.
Unfolded jet girth distribution in PbPb normalized to the number of jets that pass the $x_J$>0.4 selection. All systematic uncertainties are bin-to-bin fully correlated (allowing for sign-changes bin-to-bin).The covaraince matrices are provided for the statistical uncertainties from data and MC in this HepData record.
Covariance matrix of the statistical uncertainty in data for the unfolded jet girth distribution in PbPb for jets that pass the $x_J$>0.4 selection.The bin indices correspond to the bins used in the jet girth distribution.
Covariance matrix of the statistical uncertainty in MC for the unfolded jet girth distribution in PbPb for jets that pass the $x_J$>0.4 selection.The bin indices correspond to the bins used in the jet girth distribution.
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.
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