Deep learning jet modifications in heavy-ion collisions

Abstract

Jet interactions in a hot QCD medium created in heavy-ion collisions are conventionally assessed by measuring the modification of the distributions of jet observables with respect to the proton-proton baseline. However, the steeply falling production spectrum introduces a strong bias toward small energy losses that obfuscates a direct interpretation of the impact of medium effects in the measured jet ensemble. Modern machine learning techniques offer the potential to tackle this issue on a jet-by-jet basis. In this paper, we employ a convolutional neural network (CNN) to diagnose such modifications from jet images where the training and validation is performed using the hybrid strong/weak coupling model. By analyzing measured jets in heavy-ion collisions, we extract the original jet transverse momentum, i.e., the transverse momentum of an identical jet that did not pass through a medium, in terms of an energy loss ratio. Despite many sources of fluctuations, we achieve good performance and put emphasis on the interpretability of our results. We observe that the angular distribution of soft particles in the jet cone and their relative contribution to the total jet energy contain significant discriminating power, which can be exploited to tailor observables that provide a good estimate of the energy loss ratio. With a well-predicted energy loss ratio, we study a set of jet observables to estimate their sensitivity to bias effects and reveal their medium modifications when compared to a more equivalent jet population, i.e., a set of jets with similar initial energy. Finally, we also show the potential of deep learning techniques in the analysis of the geometrical aspects of jet quenching such as the in-medium traversed length or the position of the hard scattering in the transverse plane, opening up new possibilities for tomographic studies.

35 pages + appendices, 32 figures. Updated version for the publication in JHEP

References in corpus (21)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
• EPS09 - a New Generation of NLO and LO Nuclear Parton Distribution Functions
• The theory and phenomenology of perturbative QCD based jet quenching
• Measurements of the Nuclear Modification Factor for Jets in Pb+Pb Collisions at $\sqrt{s_{\mathrm{NN}}}=2.76$ TeV with the ATLAS Detector
• Jet physics in heavy-ion collisions
• Measurement of jet fragmentation in PbPb and pp collisions at sqrt(s[NN]) = 2.76 TeV
• Jet Flavor Classification in High-Energy Physics with Deep Neural Networks
• Measurement of jet suppression in central Pb-Pb collisions at $\sqrt{s_{\mathrm{NN}}}$ = 2.76 TeV
• A Monte Carlo Model for 'Jet Quenching'
• Medium response in JEWEL and its impact on jet shape observables in heavy ion collisions
• The wake of a quark moving through a strongly-coupled $\mathcal{N}=4$ supersymmetric Yang-Mills plasma
• Sensitivity of jet substructure to jet-induced medium response
• Picturing perturbative parton cascades in QCD matter
• Dynamical grooming of QCD jets
• Decomposing transverse momentum balance contributions for quenched jets in PbPb collisions at sqrt(s[NN]) = 2.76 TeV
• Towards Machine Learning Analytics for Jet Substructure
• Playing Tag with ANN: Boosted Top Identification with Pattern Recognition
• The JETSCAPE framework
• An Attention Based Neural Network for Jet Tagging
• Understanding wide jet suppression in data through the hybrid strong/weak coupling model