Jet momentum reconstruction in the QGP background with machine learning
arXiv:2412.06466 · doi:10.1016/j.physletb.2025.139940
Abstract
We apply a Dense Neural Network (DNN) approach to reconstruct jet momentum within a quark-gluon plasma (QGP) background, using simulated data from PYTHIA and Linear Boltzmann Transport (LBT) Models for comparative analysis. We find that medium response particles from the LBT simulation, scattered out of the QGP background but belonging to medium-modified jets, lead to oversubtraction of the background if the DNN model is trained on vacuum jets from PYTHIA simulation. By training the DNN model on quenched jets generated using LBT or the combination of jet samples from PYTHIA and LBT, we significantly reduce this prediction bias and achieve more accurate background subtraction compared to conventional Area-based and Constituent Subtraction methods widely adopted in experimental measurements. We further study the performance of these machine learning models on evaluating the nuclear modification factor of jets, and find that while the unfolding procedure is necessary for correcting residuals in reconstructed jet momenta, models trained on samples incorporating quenched jets still achieve superior accuracy than those trained on vacuum jets even after unfolding.
12 pages, 8 figures, 1 table. Updated version for the publication in PLB
References in corpus (81)
- PYTHIA 6.4 Physics and Manual
- PYTHIA 6.2 Physics and Manual
- The anti-k_t jet clustering algorithm
- An Introduction to PYTHIA 8.2
- FastJet user manual
- New Forms of QCD Matter Discovered at RHIC
- Successive Combination Jet Algorithm For Hadron Collisions
- Better Jet Clustering Algorithms
- Tuning PYTHIA 8.1: the Monash 2013 Tune
- Heavy Ion Collisions: The Big Picture, and the Big Questions
- Multiple Parton Scattering in Nuclei: Parton Energy Loss
- Applying Bayesian parameter estimation to relativistic heavy-ion collisions: simultaneous characterization of the initial state and quark-gluon plasma medium
- Multiple Scattering, Parton Energy Loss and Modified Fragmentation Functions in Deeply Inelastic eA Scattering
- Extracting jet transport coefficient from jet quenching at RHIC and LHC
- The theory and phenomenology of perturbative QCD based jet quenching
- Jet quenching in high-energy heavy-ion collisions
- Heavy Quark Energy Loss in Nuclear Medium
- Linearized Boltzmann transport model for jet propagation in the quark-gluon plasma: Heavy quark evolution
- MARTINI: An event generator for relativistic heavy-ion collisions
- Machine Learning in Nuclear Physics
- Matter in extremis: ultrarelativistic nuclear collisions at RHIC
- Linear Boltzmann Transport for Jet Propagation in the Quark-Gluon Plasma: Elastic Processes and Medium Recoil
- Bayesian analysis of heavy ion collisions with the heavy ion computational framework Trajectum
- A Hybrid Strong/Weak Coupling Approach to Jet Quenching
- Jet physics in heavy-ion collisions
- Systematic Comparison of Jet Energy-Loss Schemes in a realistic hydrodynamic medium
- Constraining the Eq. of State of Super-Hadronic Matter from Heavy-Ion Collisions
- Hard collinear gluon radiation and multiple scattering in a medium
- Pseudorapidity distribution and decorrelation of anisotropic flow within CLVisc hydrodynamics
- A transverse momentum differential global analysis of Heavy Ion Collisions
- Comparison of Jet Quenching Formalisms for a Quark-Gluon Plasma "Brick"
- JEWEL 2.0.0 - Directions for Use
- Jet Structure in Heavy Ion Collisions
- Jet quenching and medium response in high-energy heavy-ion collisions: a review
- Heavy quarks and jets as probes of the QGP
- A data-driven analysis for the temperature and momentum dependence of the heavy quark diffusion coefficient in relativistic heavy-ion collisions
- Full jet in quark-gluon plasma with hydrodynamic medium response
- Determining the jet transport coefficient from inclusive hadron suppression measurements using Bayesian parameter estimation
- Estimating initial state and quark-gluon plasma medium properties using a hybrid model with nucleon substructure calibrated to -Pb and Pb-Pb collisions at TeV
- Multiple Parton Scattering in Nuclei: Beyond Helicity Amplitude Approximation
- Interplaying mechanisms behind single inclusive jet suppression in heavy-ion collisions
- Multiple jets and -jet correlation in high-energy heavy-ion collisions
- Sensitivity of jet substructure to jet-induced medium response
- Multistage Monte-Carlo simulation of jet modification in a static medium
- Exploring QCD matter in extreme conditions with Machine Learning
- Principal component analysis of event-by-event fluctuations
- (3+1)-D viscous hydrodynamics CLVisc at finite net baryon density: identified particle spectra, anisotropic flows and flow fluctuations across BES energies
- Probing the Short-Distance Structure of the Quark-Gluon Plasma with Energy Correlators
- Nuclear modification of leading hadrons and jets within a virtuality ordered parton shower
- Medium modification of -jet fragmentation functions in Pb+Pb collisions at LHC
- Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning
- Heavy quark potential in quark-gluon Plasma: Deep neural network meets lattice quantum chromodynamics
- Subleading harmonic flows in hydrodynamic simulations of heavy ion collisions
- Flavor hierarchy of jet quenching in relativistic heavy-ion collisions
- Nuclear modification of jet shape for inclusive jets and -jets at the LHC energies
- Multiple Parton Scattering in Nuclei: Heavy Quark Energy Loss and Modified Fragmentation Functions
- Information field based global Bayesian inference of the jet transport coefficient
- Jet modification with medium recoil in quark-gluon plasma
- Machine-learning-based identification for initial clustering structure in relativistic heavy-ion collisions
- Pileup and Underlying Event Mitigation with Iterative Constituent Subtraction
- Measurement of the radius dependence of charged-particle jet suppression in Pb-Pb collisions at = 5.02 TeV
- Deep learning jet modifications in heavy-ion collisions
- Bulk medium evolution has considerable effects on jet observables
- Machine Learning based jet momentum reconstruction in heavy-ion collisions
- Linear Boltzmann transport for jet propagation in the quark-gluon plasma: Inelastic processes and jet modification
- Principal Component Analysis of collective flow in Relativistic Heavy-Ion Collisions
- Estimation of Impact Parameter and Transverse Spherocity in heavy-ion collisions at the LHC energies using Machine Learning
- Medium Response and Jet-Hadron Correlations in Relativistic Heavy-Ion Collisions
- Deep-learning quasi-particle masses from QCD equation of state
- Deep learning assisted jet tomography for the study of Mach cones in QGP
- Constraining through high-p theory and data
- Constraining the equation of state with heavy quarks in the quasi-particle model of QCD matter
- Final State Gluon Emission in Deep-Inelastic Scattering at Next-to-Leading Twist
- Effects of the formation time of parton shower on jet quenching in heavy-ion collisions
- Identifying quenched jets in heavy ion collisions with machine learning
- Parton Energy Loss and the Generalized Jet Transport Coefficient
- Determination of impact parameter in high-energy heavy-ion collisions via deep learning
- Interpretable Machine Learning Methods Applied to Jet Background Subtraction in Heavy Ion Collisions
- Revisiting heavy quark radiative energy loss in nuclei within the high-twist approach
- Jet substructure observables for jet quenching in Quark Gluon Plasma: a Machine Learning driven analysis
- Classification of quark and gluon jets in hot QCD medium with deep learning