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.

Highlights

  • 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

  • The jet quenching phenomenon has been primarily attributed to the observed strong suppression of intermediate-pT hadrons at the Relativistic Heavy-Ion Collider (RHIC) [5, 6] and years later via the dijet asymmetry and the suppression of high energy reconstructed jets at the Large Hadron Collider (LHC) [7,8,9,10,11,12,13,14]

  • The ability to properly correlate the level of medium modifications to intrinsic properties of individually reconstructed jets will help enhance the potential of these probes to accurately diagnose properties of hot QCD medium, provided that the mechanisms by which jets interact with the medium are under good theoretical control

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Summary

General setup and main variables

We describe the particular Monte Carlo event generator that was used to generate the analyzed jet images. We discuss the main physical observables that will be used for the analysis. We describe the machine learning frameworks used in this work

Modeling energy loss using the hybrid model
Jet energy loss ratio χjh and traversed path-length L
Matching procedure
Observables
Network architectures and task description
Jet sample generation and re-weighting procedure
Jet image analysis
Jet image and pre-processing
A first look at correlations
Prediction performance
Sensitivity to soft and large-angle radiation
Applications
Sensitivity of observables to in-medium modification
Groomed observables
Ungroomed observables
Tomography
Conclusions and outlook
A Correlations between jet observables
B Prediction performance versus jet observables

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