Related papers: A Novel Deep Learning Method for Detecting Nucleon…
The detection of gravitational waves from core-collapse supernova (CCSN) explosions is a challenging task, yet to be achieved, in which it is key the connection between multiple messengers, including neutrinos and electromagnetic signals.…
Basic problems of the semiclassical microscopic modelling of strongly interactingsystems are discussed within the framework of Quantum Molecular Dynamics (QMD). This model allows to study the influence of several types of nucleonic…
The effect of initial correlations between nucleons on the nuclear break-up mechanism is studied. A quantum transport theory which extends standard mean-field approach is developed to incorporate short range pairing correlation as well as…
The search for the chiral magnetic effect (CME) in relativistic heavy-ion collisions (HICs) is challenged by significant background contamination. We present a novel deep learning approach based on a U-Net architecture to time-reversely…
Quantum Molecular Dynamics models (QMD) are Monte Carlo approaches targeted at the description of nucleon-ion and ion-ion collisions. We have developed a QMD code, which has been used for the simulation of the fast stage of ion-ion…
Jet modification in heavy-ion collisions provides microscopic access to the properties of the quark-gluon plasma. However, conventional approaches based on traditional global observables, such as \(R_{AA}\), capture limited information…
We propose a novel approach to the analysis of experimental data obtained in relativistic nucleus-nucleus collisions which borrows from methods developed within the context of Random Matrix Theory. It is applied to the detection of…
A method for reconstructing the direction of a fast neutron source using a segmented organic scintillator-based detector and deep learning model is proposed and analyzed. The model is based on recurrent neural network, which can be trained…
We present an overview of selected aspects of ultrarelativistic nucleus-nucleus collisions, a research program devoted to the study of strongly interacting matter at high energy densities and in particular to the characterization of the…
Using covariance analysis, we quantify the correlations between the interaction parameters in a transport model and the observables commonly used to extract information of the Equation of State of Asymmetric Nuclear Matter in experiments.…
Theoretical study on hypernuclear systems is important to know the nature of hyperon-nucleon and hyperon-hyperon interaction as only hypernuclear systems give the scope of knowing these interactions. A hypernucleus, in addition to the…
We present results of hydrodynamic modelling of Au-Au collisions from $\sqrt{s_{NN}}$ = 7.7 to 200 GeV. Our simulations have three novel components. Firstly, we use a Linear EXtrapolation of Ultrarelativistic nucleon-nucleon Scattering to…
The finite nuclear thickness affects the energy density $\epsilon(t)$ and conserved-charge densities such as the net-baryon density $n_B(t)$ produced in heavy ion collisions. While the effect is small at high collision energies where the…
In relativistic heavy-ion collisions, the extractions of properties of quark-gluon plasma (QGP) are hindered by a limited understanding of its initial conditions, where the nuclear structure of the colliding ions play a significant role. In…
Recent progress of the quantum molecular dynamics model for describing the dynamics of heavy-ion collisions is viewed, in particular the nuclear fragmentation, isospin physics, particle production and in-medium effect, hadron-induced…
We report a systematic comparison of the recently measured cumulants of the net-proton distributions for 0-5\% central Au+Au collisions in the first phase of the Beam Energy Scan (BES) Program at the Relativistic Heavy Collider facility to…
This work presents a Bayesian inference study for relativistic heavy-ion collisions in the Beam Energy Scan program at the Relativistic Heavy-Ion Collider. The theoretical model simulates event-by-event (3+1)D collision dynamics using…
We suggest to explore an entirely new method to experimentally and theoretically study the phase diagram of strongly interacting matter based on the triple nuclear collisions (TNC). We simulated the TNC using the UrQMD 3.4 model at the beam…
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…
Using deep convolutional neural network (CNN), the nature of the QCD transition can be identified from the final-state pion spectra from hybrid model simulations of heavy-ion collisions that combines a viscous hydrodynamic model with a…