Related papers: Machine learning approach to QCD kinetic theory
The early time dynamics of heavy ion collisions can be described by classical fields in an approximation of Quantum ChromoDynamics (QCD) called Color Glass Condensate (CGC). Monte-Carlo sampling of the color charge for the incoming nuclei…
We derive a quantum kinetic theory for QED based on Kadanoff-Baym equations for Wigner functions. By assuming parity invariance and considering a complete set of self-energy diagrams, we find the resulting kinetic theory expanded to lowest…
In the context of high-energy physics, a reliable description of the parton-level kinematics plays a crucial role for understanding the internal structure of hadrons and improving the precision of the calculations. Here, we study the…
This report summarizes the results of the work done in collaboration with K. Rajagopal and E. Shuryak. We analyze the physics behind the event-by-event fluctuations in heavy ion collisions. Using thermodynamic description of the ensemble of…
We study the beam energy dependence of equilibration process and space-time characteristics of participant and spectator matter. For this, we simulated the semi-central collisions of $^{40}Ca+ ^{40}Ca$ at incident energies of 400, 600 and…
Machine Learning (ML) techniques have been employed for the high energy physics (HEP) community since the early 80s to deal with a broad spectrum of problems. This work explores the prospects of using Deep Learning techniques to estimate…
The study of quantum chromodynamics (QCD) over the past quarter century has had relatively little impact on the traditional approach to the low-energy nuclear many-body problem. Recent developments are changing this situation. New…
The total cross section for scattering virtual photons at high energy is sensitive to pomeron physics. If the photons are sufficiently virtual, QCD perturbation theory applies, so that the reaction probes the short distance pomeron. We…
We present a novel saturation and leading order collinear factorization based Monte-Carlo implementation of the EKRT model for computing QCD matter initial states in high-energy nuclear collisions. As new features the MC implementation…
Heavy ion collisions are rich and complex systems that involve different aspects of QCD and electromagnetic phenomena. From head-on collisions to the case in which the nuclei miss each other, many of QCD and photo-induced probes are being…
Quantum Extreme Learning Machine (QELM) is an emerging hybrid quantum machine learning framework that leverages quantum system dynamics to enhance classical models. However, QELM can suffer from the exponential concentration problem, where…
Quasiparticle dynamics in relativistic plasmas associated with hot, weakly-coupled gauge theories (such as QCD at asymptotically high temperature $T$) can be described by an effective kinetic theory, valid on sufficiently large time and…
Machine Learning (ML) algorithms have been demonstrated to be capable of predicting impact parameter in heavy-ion collisions from transport model simulation events with perfect detector response. We extend the scope of ML application to…
High energy hadron-hadron collisions are discussed. It is argued that soft collisions should involve in an essential way nonperturbative QCD. A way is outlined how to calculate properties of high energy elastic hadron-hadron scattering…
Computational methods both open the frontiers of economic analysis and serve as a bottleneck in what can be achieved. We are the first to study whether Quantum Monte Carlo (QMC) algorithm can improve the runtime of economic applications and…
Nucleon-ion and ion-ion collisions at non relativistic bombarding energies can be described by means of Monte Carlo approaches, such as those based on the Quantum Molecular Dynamics (QMD) model. We have developed a QMD code, to simulate the…
Quantum machine learning could possibly become a valuable alternative to classical machine learning for applications in High Energy Physics by offering computational speed-ups. In this study, we employ a support vector machine with a…
Machine learning algorithms provide a new perspective on the study of physical phenomena. In this paper, we explore the nature of quantum phase transitions using multi-color convolutional neural-network (CNN) in combination with quantum…
A user friendly scheme based on the quantum kinetic equation is developed for studying thermal transport phenomena in the presence of interactions and disorder. We demonstrate that this scheme is suitable for both a systematic perturbative…
Using QCD kinetic theory, we study momentum broadening of jets using the full broadening probability (elastic collision kernel) $C(\mathbf{q_\perp})$ extracted during simulations of the nonequilibrium initial stages in heavy-ion collisions.…