Related papers: Classifiers for centrality determination in proton…
This paper presents a technique that combines the occurrence of certain events, as observed by different sensors, in order to detect and classify objects. This technique explores the extent of dependence between features being observed by…
The experimental results on the relation between the number of events, the angular distributions of protons and full number of protons are presented for ${}^{12}CC$-interactions at the momentum of 4.2 A GeV/c. The influence of nuclear…
The ALICE detector at the LHC is used to study the properties of the Quark-Gluon Plasma produced in heavy-ion collisions. As a reference measurement, also the analysis of proton-proton (pp) collisions is very important. In the study…
In this talk I'll review the present status of charged particle multiplicity measurements from heavy-ion collisions. The characteristic features of multiplicity distributions obtained in Au+Au collisions will be discussed in terms of…
$\alpha$-clustering structure is a significant topic in light nuclei. A Bayesian convolutional neural network (BCNN) is applied to classify initial non-clustered and clustered configurations, namely Woods-Saxon distribution and…
The possible transition of nuclear matter to a deconfined phase in relativistic nucleus-nucleus collisions is explored by a systematic variation of the collision system by means of system size, beam energy and centrality of the collision…
In heavy-ion ({\it A-A}) collisions, the correlations among the particles produced across wide range in rapidity, probe the early stages of the reaction. The analyses of forward-backward multiplicity correlations in these collisions are…
In this paper, a "centrality" estimator based on flattenicity ($\rho$) is studied in proton-led (p-Pb) collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV using PYTHIA 8 Angantyr. Although Angantyr is still under development, the existing…
A new method of event characterization based on Deep Learning is presented. The PointNet models can be used for fast, online event-by-event impact parameter determination at the CBM experiment. For this study, UrQMD and the CBM detector…
The proton and neutron inclusive distributions in the projectile fragmentation region of $pA$ collisions are studied in the valon model. Momentum degradation and flavor changes due to the nuclear medium are described at the valon level…
A convolutional neural network-based classifier is elaborated to retrace the initial orientation of deformed nucleus-nucleus collisions by integrating multiple typical experimental observables. The isospin-dependent…
Novelty detection is the machine learning task to recognize data, which belong to an unknown pattern. Complementary to supervised learning, it allows to analyze data model-independently. We demonstrate the potential role of novelty…
The centrality dependence of the mean charged-particle multiplicity as a function of pseudorapidity is measured in approximately 1 $\mu$b$^{-1}$ of proton--lead collisions at a nucleon--nucleon centre-of-mass energy of…
Proton-nucleus collisions (p+A) at LHC energies provide a rigorous test of the Color Glass Condensate (CGC), a model proposed to describe the high energy limit of Quantum Chromodynamics. In the CGC the average multiplicity of charged…
Study of the characteristic properties of charged particle production in hadron-nucleus collisions at high energies, by utilising the approaches from different statistical models is performed.~Predictions from different approaches using the…
The study of event-by-event fluctuations of identified hadrons may reveal the degrees of freedom of the strongly interacting matter created in heavy-ion collisions and the underlying dynamics of the system. The observable $\nu_{dyn}$, which…
We studied the centrality selection effect on cumulants (up to fourth order) and the cumulants ratios of net-proton multiplicity distributions in Au+Au collisions at $\sqrt{s_{\mathrm{NN}}}$ = 7.7, 19.6 and 200 GeV from UrQMD model. The…
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…
Particle correlations and particle multiplicity distributions cannot be approached independently: a unified description of correlations and multiplicity distributions is always needed in order to understand the underlying dynamics in high…
The ALICE Collaboration reports its first LHC Run 3 measurements of charged-particle pseudorapidity density at midrapidity in Pb-Pb collisions at a centre-of-mass energy per nucleon pair of $\sqrt{s_{\mathrm{NN}}}=5.36$ TeV. Particle…