Probing heavy ion collisions using quark and gluon jet substructure with machine learning
arXiv:1808.04708 · doi:10.1016/j.nuclphysa.2018.11.009
Abstract
Understanding the inner working of the quark-gluon plasma requires complete and precise jet substructure studies in heavy ion collisions. In this proceeding we discuss the use of quark and gluon jets as independent probes, and how their classification allows us to uncover regions of QCD phase space sensitive to medium dynamics. We introduce the telescoping deconstruction (TD) framework to capture complete jet information and show that TD observables reveal fundamental properties of quark and gluon jets and their modifications in the medium. We draw connections to soft-drop subjet distributions and illuminate medium-induced jet modifications using Lund diagrams. The classification is also studied using a physics-motivated, multivariate analysis of jet substructure observables. Moreover, we apply image-recognition techniques by training a deep convolutional neural network on jet images to benchmark classification performances. We find that the quark gluon discrimination performance worsens in \textsc{Jewel}-simulated heavy ion collisions due to significant soft radiation affecting soft jet substructures. This work suggests a systematic framework for jet studies and facilitates direct comparisons between theoretical calculations and measurements in heavy ion collisions.
arXiv comment4 pages, 5 figures. Talk presented at the XXVIIth International Conference on Ultrarelativistic Nucleus-Nucleus Collisions (Quark Matter 2018), 13-19 May 2018, Venice, Italy
References in corpus (10)
- Soft Drop
- Deep learning in color: towards automated quark/gluon jet discrimination
- Modification of jet shapes in PbPb collisions at sqrt(s[NN]) = 2.76 TeV
- A Monte Carlo Model for 'Jet Quenching'
- Measurement of the splitting function in pp and PbPb collisions at 5.02 TeV
- Towards the understanding of jet shapes and cross sections in heavy ion collisions using soft-collinear effective theory
- Probing the hardest branching of jets in heavy ion collisions
- Telescoping Jets: Multiple Event Interpretations with Multiple R's
- Telescoping jet substructure
- Exploring the Phase Space of Jet Splittings at ALICE using Grooming and Recursive Techniques
Cited by in corpus (8)
- Machine Learning in Nuclear Physics
- Exploring QCD matter in extreme conditions with Machine Learning
- Unsupervised Outlier Detection in Heavy-Ion Collisions
- Deep learning assisted jet tomography for the study of Mach cones in QGP
- Applications of deep learning to relativistic hydrodynamics
- Unraveling Gluon Jet Quenching through Production in Heavy-Ion Collisions
- Report from the A.I. For Nuclear Physics Workshop
- Apples to Apples in Jet Quenching: robustness of Machine Learning classification of quenched jets to Underlying Event contamination