Related papers: A deep learning approach for predicting multiple o…
Over the last years, machine learning tools have been successfully applied to a wealth of problems in high-energy physics. A typical example is the classification of physics objects. Supervised machine learning methods allow for significant…
A deep learning based method with Convolutional Neural Network (CNN) algorithm is developed for simultaneous determination of the Elliptic Flow coefficient ($v_{2}$) and the Impact Parameter in Heavy-Ion Collisions at relativistic energies.…
A novel method for identifying the nature of QCD transitions in heavy-ion collision experiments is introduced. PointNet based Deep Learning (DL) models are developed to classify the equation of state (EoS) that drives the hydrodynamic…
Combining the hydrodynamic model (Hydro code) and the transport model (PACIAE model), we present the Hydro-PACIAE hybrid model. We use the Hydro-PACIAE hybrid model to calculate Au+Au collisions at $\sqrt{s_{NN}}$=130 GeV and Pb+Pb…
The multiplicity of charged particles produced in Au+Au collisions as a function of energy, centrality, rapidity and azimuthal angle has been measured with the PHOBOS detector at RHIC. These results contribute to our understanding of the…
The deep learning technique has been applied for the first time to investigate the possibility of centrality determination in terms of the number of participants ($N_{\mathrm{part}}$) in high-energy heavy-ion collisions. For this purpose,…
We study multi-particle azimuthal correlations in relativistic heavy-ion collisions at a center of mass energy of 200 GeV. We use the IP-Glasma model to initialize the viscous hydrodynamic simulation MUSIC and employ the UrQMD transport…
We study the directed flow of identified particles in Au+Au collisions at $\sqrt{s_\text{NN}}=7.7$ to 62.4 GeV. The Glauber model is extended to include both a tilted deformation of the QGP fireball with respect to the longitudinal…
Relativistic hydrodynamics has been extensively applied to high energy heavy-ion collisions. We review hydrodynamic calculations for Au+Au collisions at RHIC energies and provide a comprehensive comparison between the model and experimental…
Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. To establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark…
The application of deep learning techniques using convolutional neural networks to the classification of particle collisions in High Energy Physics is explored. An intuitive approach to transform physical variables, like momenta of…
We have developed a next-generation hybrid event-by-event three-fluid hydrodynamic model, suitable for simulations of heavy-ion collisions in the energy range from few up to tens of GeV per colliding NN pair. At such energies the…
A central challenge in high-energy nuclear physics is to extract informative features from the high-dimensional final-state data of heavy-ion collisions (HIC) in order to enable reliable downstream analyses. Traditional approaches often…
Exploiting the first measurements of the same ion species in O+O collisons at RHIC and LHC, we propose an experimentally accessible observable to distinguish whether collective behaviour builds up through a hydrodynamic expansion of a…
The production of light hypernuclei in relativistic heavy-ion collisions provides a unique opportunity to probe hyperon--nucleon interactions and possible three-body forces, which are central to the resolution of the hyperon puzzle in…
We investigate the space-time evolution of ultrarelativistic Au-Au collisions at full RHIC energy using a schematic model of the expansion. Assuming a thermally equilibrated system, we can adjust the essential scale parameters of this model…
The study of flow can provide information on the initial state dynamics and the degree of equilibration attained in heavy ion collisions. This contribution presents results for both elliptic and directed flow as determined from data…
Description of relativistic heavy-ion collisions at the energies of RHIC Beam Energy Scan program with fluid dynamic approach poses several challenges, one of which being a complex geometry and a longer duration of the pre-hydrodynamic…
Using a hybrid (viscous hydrodynamics + hadronic cascade) framework, we model the bulk dynamical evolution of relativistic heavy-ion collisions at Relativistic Heavy Ion Collider (RHIC) Beam Energy Scan (BES) collision energies, including…
We discuss expectations of saturation physics for various observables in heavy ion collisions. We show how simple saturation-inspired assumptions about particle production in heavy ion collisions lead to Kharzeev-Levin-Nardi model.…