Light-quark and gluon jet discrimination in pp collisions at $\sqrt{s}$ = 7 TeV with the ATLAS detector

TL;DR

This work develops a data-driven, likelihood-based quark- and gluon-jet discriminant for ATLAS 7 TeV pp collisions, using track-based observables to mitigate calorimeter pile-up effects. By extracting pure quark/gluon templates from dijet and γ+jet samples and validating them with event-level selections, the authors quantify discrimination performance and systematic uncertainties, revealing notable differences between data and MC models. The study shows attainable gluon-jet rejection levels at fixed light-quark efficiencies, underscores the importance of data-driven calibration, and highlights EEC angularities as a complementary avenue with beta-dependent behavior. Overall, the results provide a robust framework for jet-flavour tagging in hadron colliders while stressing sample-dependence and generator model implications for MC-driven taggers.

Abstract

A likelihood-based discriminant for the identification of quark- and gluon-initiated jets is built and validated using 4.7 fb$^{-1}$ of proton-proton collision data at $\sqrt{s}$ = 7 TeV collected with the ATLAS detector at the LHC. Data samples with enriched quark or gluon content are used in the construction and validation of templates of jet properties that are the input to the likelihood-based discriminant. The discriminating power of the jet tagger is established in both data and Monte Carlo samples within a systematic uncertainty of 10-20%. In data, light-quark jets can be tagged with an efficiency of 50% while achieving a gluon-jet mis-tag rate of 25% in a $p_T$ range between 40 GeV and 360 GeV for jets in the acceptance of the tracker. The rejection of gluon-jets found in the data is significantly below what is attainable using a Pythia 6 Monte Carlo simulation, where gluon-jet mis-tag rates of 10% can be reached for a 50% selection efficiency of light-quark jets using the same jet properties.

Figures (8)

• Figure 1: Separation power provided by different variables between quark- and gluon-jets as a function of jet $p_{\mathrm{T}}$ in the Pythia 6 dijet MC simulation for jets with $|\eta|<0.8$ built with the anti-$k_t$ algorithm with $R=0.4$.
• Figure 2: Average (a,c) $n_{\rm trk}$ and (b,d) track width for quark- (solid symbols) and gluon-jets (open symbols) as a function of reconstructed jet $p_{\mathrm{T}}$ for isolated jets with $|\eta|<0.8$. Results are shown for distributions obtained using the in-situ extraction method in Pythia 6 simulation (black circles, (a,b)) or data (black circles, (c,d)), as well as for labelled jets in the dijet sample (triangles) and in the $\gamma\text{+jet}\xspace$ sample (squares). The error bars represent only statistical uncertainties. Isolated jets are reconstructed using the anti-$k_t$ jet algorithm with radius parameter $R=0.4$. The bottom panels show the ratio of the results obtained with the in-situ extraction method to the results in the dijet and $\gamma\text{+jet}\xspace$ MC samples.
• Figure 3: Top, the jet (a) $n_\text{trk}$ and (b) track width as a function of $p_{\mathrm{T}}$ for jets in a gluon-jet-enriched trijet sample (triangles) compared to gluon-jet extracted templates (circles) for $|\eta|<0.8$. Bottom, the jet (c) $n_\text{trk}$ and (d) track width as a function of $p_{\mathrm{T}}$ for jets in a quark-jet-enriched $\gamma$+jet sample (triangles) compared to quark-jet extracted templates (circles) for jets with $|\eta|<0.8$. Jets are reconstructed with the anti-$k_t$ algorithm with $R=0.4$. The bottom panels of the figures show the ratios of the results found in the enriched sample to the extracted results. Error bars on the points for the enriched sample correspond to statistical uncertainties. The inner shaded band around the circles and in the ratio represents statistical uncertainties on the extracted results, while the outer error band represents the combined systematic and statistical uncertainties.
• Figure 4: The jet (a) $n_\text{trk}$ and (b) track width as a function of $p_{\mathrm{T}}$ for quark-jets in an OS minus SS $W$+jet sample (see text) for $|\eta|<0.8$ in Pythia 6 MC simulation and in data. The panels show the ratio of the results in data to those in MC simulation.
• Figure 5: Gluon-jet efficiency as a function of quark-jet efficiency calculated using jet properties extracted from data (solid symbols) and from MC-labelled jets from the dijet Pythia 6 (empty squares) and Herwig++ (empty diamonds) samples. Jets with (a) $60<p_{\mathrm{T}}<80$Ge V and (b) $210<p_{\mathrm{T}}<260$Ge V and $|\eta|<0.8$ are reconstructed with the anti-$k_t$ algorithm with $R=0.4$. The shaded band shows the total systematic uncertainty on the data. The bottom of the plot shows the ratios of each MC simulation to the data. The error bands on the performance in the data are drawn around 1.0.