Apples to Apples in Jet Quenching: robustness of Machine Learning classification of quenched jets to Underlying Event contamination

TL;DR

The paper tackles the challenge of identifying true jet quenching signals in heavy-ion collisions when underlying-event contamination can mimic medium-induced modifications. It uses Jewel-based simulations to create realistic PbPb baselines with medium response and UE, and contrasts them with pp baselines embedded in PbPb-like UE, enabling apples-to-apples comparisons. The study finds that several observables, notably those based on transverse momentum information (e.g., $R_{AA}$ and $p_T^D$), are robust to UE contamination, while angular-sensitive observables (jet profiles, Lund planes) are more UE-sensitive. A Boosted Decision Tree classifier demonstrates robust discrimination between quenched and unquenched jets even in the presence of MR and UE, aided by the Energy-Energy Correlator as an interpretable cross-check; this supports applying ML-based quenching studies to experimental data with realistic baselines.

Abstract

Progress in the theoretical understanding of parton branching dynamics within an expanding Quark Gluon Plasma relies on detailed and fair comparisons with experimental data for reconstructed jets. Such comparisons are only meaningful when the computed jet, be it analytically or via event generation, accounts for the complexity of jets reconstructed in the challenging environment of heavy-ion collisions. Jet reconstruction in heavy ion collisions involves a necessarily imperfect subtraction of the large and fluctuating underlying event: reconstructed jets always include underlying event contamination. To identify true jet quenching effects, modifications due to the interaction of the branching partonic system with the Quark Gluon Plasma, we establish a baseline that accounts for possible background contamination on unmodified jets. In practical terms, jet quenching effects are only those not present in jets produced in proton-proton collisions that have been embedded in a realistic heavy-ion background and where subtraction has been carried out analogously to that in the heavy ion case. With this setup, we assess the sensitivity to underlying event of commonly discussed jet quenching observables and its impact on the robustness of Machine Learning studies, aimed at classifying jets according to their degree of modification by the Quark Gluon Plasma, that rely on those observables. We find the discrimination power of a simple Boosted Decision Tree to be robust in the realistic scenario where both medium response and underlying event are present, giving support to portability to the experimental context.

Figures (22)

• Figure 1: Residual UE contamination, after embedding and subtraction, on: (a) relative jet transverse momentum $\frac{\delta p_T}{p_T^{[i]}} = \frac{p_T^{[i+\mathbf{UE}]} - p_T^{[i]}}{p_T^{[i]}}$; (b) jet mass normalized to the jet initial momentum $\frac{\delta m}{p_T^{[i]}} = \frac{m^{[i+\mathbf{UE}]} - m^{[i]}}{p_T^{[i]}}$; (c) pseudorapidity $\delta \eta = \eta^{[i+\mathbf{UE}]} - \eta^{[i]}$; and (d) azimuthal angle $\delta \varphi = |\varphi^{[i+\mathbf{UE}]} - \varphi^{[i]}|$.
• Figure 2: Inclusive jet $R_{AA}$ without (blue) and with (red) the inclusion of UE contamination. The pp + UE to pp (green) and PbPb + MR + UE to PbPb + MR (orange) ratios show the effect of the contamination separately on each sample. (a) for ungroomed jets, with experimental data ATLAS:2018gwx shown in black, and (b) for Soft Drop groomed jets.
• Figure 3: Transverse momentum fraction $x_j$ for pp (blue), pp + UE (red), PbPb + MR (green), and PbPb + MR + UE (orange). Ratios of PbPb + MR to pp without (green) and with (orange) UE are shown in the lower panels. (a) for ungroomed dijet pairs, and (b) for Soft Drop groomed dijet pairs.
• Figure 4: Transverse momentum dispersion $p_T^D$. (a) distributions for pp (blue), pp + UE (red), PbPb + MR (green), and PbPb + MR + UE (orange); (b) ratios of PbPb + MR to pp distributions without (blue) and with (red) UE, and the effect of UE contamination for pp (green) and PbPb + MR (orange) separately.
• Figure 5: Ratios of PbPb + MR to pp jet profiles, without UE (blue) and with UE (red), and the effect of UE contamination for pp (green) and PbPb + MR (orange) separately. (a) inclusive jets, experimental data (black) from CMS:2018zze; (b) leading jet in a dijet pair, experimental data (black) from CMS:2021nhn; and (c) subleading jet in a dijet pair, experimental data (black) from CMS:2021nhn.