Jet tomography in heavy ion collisions with deep learning

Abstract

Deep learning techniques have the power to identify the degree of modification of high energy jets traversing deconfined QCD matter on a jet-by-jet basis. Such knowledge allows us to study jets based on their initial, rather than final energy. We show how this new technique provides unique access to the genuine configuration profile of jets over the transverse plane of the nuclear collision, both with respect to their production point and their orientation. Effectively removing the selection biases induced by final-state interactions, one can in this way analyse the potential azimuthal anisotropies of jet production associated to initial-state effects. Additionally, we demonstrate the capability of our new method to locate with precision the production point of a dijet pair in the nuclear overlap region, in what constitutes an important step forward towards the long term quest of using jets as tomographic probes of the quark-gluon plasma.

9 pages, 7 figures. Updated version for the publication in Phys. Rev. Lett

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Cited by in corpus (7)

• Dynamically groomed jet radius in heavy-ion collisions
• Quarks and gluons in the Lund plane
• Disentangling Jet Modification in Jet Simulations and in Z+Jet Data
• Quantum partonic transport in QCD matter
• Deep learning assisted jet tomography for the study of Mach cones in QGP
• Identifying quenched jets in heavy ion collisions with machine learning
• Studying the QGP with Jets at the LHC and RHIC