Overview: Jet quenching with machine learning

Jets are suppressed and modified in heavy ion collisions, which serve as powerful probes to the properties of the quark-gluon plasma (QGP). Attributed to the abundant information carried by the jet constituents and reconstructed substructures, plenty of interesting applications of machine learning techniques have been made on a jet-by-jet basis to study the jet quenching phenomena. Here we review recent proceedings on this topic including the tasks of reconstructing jet momentum in heavy ion collisions, classifying quenched jets and unquenched jets, identifying jet energy loss, locating the jet creation points as well as distinguishing between quark- and gluon-initiated jets in the QGP. Such jet-by-jet analyses will allow us to have a better handle on the jet reconstruction and selections to investigate the effects of jet modifications and push forward the long-standing goal of jet tomographic probes of the QGP.Comment: 11 pages, 11th International Conference on Hard and Electromagnetic Probes of High-Energy Nuclear Collisions (Hard Probes 2023

Heavy quark radiative energy loss in nuclei within the improved high-twist approach

In this proceedings, we review our recent work on the heavy quark radiative energy loss in nuclei due to multiple parton scattering within the recently improved high-twist approach, where gauge invariance can be ensured by a delicate setup of the initial partons' transverse momenta. Our new result is consistent with the previous calculations of light quark energy loss in the massless limit and heavy quark energy loss in the soft gluon radiation limit, respectively. We show numerically the correction to the heavy quark energy loss as compared with previous result and with soft gluon radiation approximation. The necessity to go beyond soft gluon radiation limit is demonstrated for a global description of light and heavy flavor data in heavy-ion collisions.Comment: 6 pages, 4 figures, 10th International Conference on Hard and Electromagnetic Probes of High-Energy Nuclear Collisions (Hard Probes 2020

Critical behaviors near the (tri-)critical end point of QCD within the NJL model

We investigate the dynamical chiral symmetry breaking and its restoration at finite density and temperature within the two-flavor Nambu-Jona-Lasinio model, and mainly focus on the critical behaviors near the critical end point (CEP) and tricritical point (TCP) of quantum chromodynamics. The multi-solution region of the Nambu and Wigner ones is determined in the phase diagram for the massive and massless current quark, respectively. We use the various susceptibilities to locate the CEP/TCP and then extract the critical exponents near them. Our calculations reveal that the various susceptibilities share the same critical behaviors for the physical current quark mass, while they show different features in the chiral limit

Quark self-energy and condensates in NJL model with external magnetic field

In a one-flavor NJL model with a finite temperature, chemical potential, and external magnetic field, the self-energy of the quark propagator contains more condensates besides the vacuum condensate. We use Fierz identity to identify the self-energy and propose a self-consistent analysis to simplify it. It turns out that these condensates are related to the chiral separation effect and spin magnetic moment.publishedVersio

Jet Tomography in Heavy-Ion Collisions with Deep Learning

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. By effectively removing the selection biases induced by final-state interactions, one can analyze 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 toward the long term quest of using jets as tomographic probes of the quark-gluon plasma.publishedVersio

Jet tomography in heavy ion collisions with deep learning

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 remarkable 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.Comment: 8 pages, 4 figure

Deep learning jet modifications in heavy-ion collisions

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.publishedVersio