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In this paper, we propose a parameterization which is based on a phenomenological model involving the wounded quarks interactions for explaining the average charged particle multiplicity $\ < n_{ch}\ >$, the central pseudo-rapidity density…
Principal components analysis (PCA) is a classical method for the reduction of dimensionality of data in the form of n observations (or cases) of a vector with p variables. For a simple model of factor analysis type, it is proved that…
We investigate the centrality dependence of the pT-correlations in the event-by-event analysis of relativistic heavy-ion collisions at RHIC made recently by the PHENIX and STAR Collaborations. We notice that sigma^2 dynamical scales to a…
Results of a systematic study of fully integrated particle multiplicities in central Au-Au and Pb-Pb collisions at beam momenta 1.7 A GeV, 11.6 A GeV (Au-Au) and 158 A GeV (Pb-Pb) using a statistical-thermal model are presented. The close…
Principal Component Analysis (PCA) is a method for estimating a subspace given noisy samples. It is useful in a variety of problems ranging from dimensionality reduction to anomaly detection and the visualization of high dimensional data.…
As it is well known by now, the pre-partonic phase in hadron collisions is successfully described by the Color Glass Condensate (CGC) approach. Previous studies, based on experimental data obtained on a wide range of energies at the…
A two-component (soft + hard) model (TCM) of hadron production in high-energy nuclear collisions is applied to ensemble-mean $p_t$ (denoted by $\bar p_t$) data for $p$-$p$, $p$-Pb and Pb-Pb collisions from the relativistic heavy ion…
The striking resemblance of high multiplicity proton-proton (pp) collisions at the LHC to heavy ion collisions challenges our conventional wisdom on the formation of the Quark-Gluon Plasma (QGP). A consistent explanation of the collectivity…
Principal Component Analysis (PCA) is a commonly used tool for dimension reduction in analyzing high dimensional data; Multilinear Principal Component Analysis (MPCA) has the potential to serve the similar function for analyzing tensor…
Measurements made by the ALICE Collaboration of single- and two-particle distributions in high-energy pp and p-Pb collisions are used to characterize the interactions in small collision systems, tune models of particle production in QCD,…
Measurements of event-ensemble mean transverse momentum $\langle p_t \rangle$ vs charged-hadron multiplicity $n_{ch}$ for $p_t$ spectra from 5 TeV p-Pb and 2.76 TeV Pb-Pb collisions and from p-p collisions for several energies have been…
Model identification is a crucial problem in chemical industries. In recent years, there has been increasing interest in learning data-driven models utilizing partial knowledge about the system of interest. Most techniques for model…
In recent years, Artificial Intelligence techniques have proved to be very successful when applied to problems in physical sciences. Here we apply an unsupervised Machine Learning (ML) algorithm called Principal Component Analysis (PCA) as…
Collisions among heavy ions, like Pb or Au, are a great tool to study the theory of strong interactions, that is Quantum Chromodynamics (QCD). In particular, these experiments are able to give insights on all the complex phases of matter…
In this paper, our goal is to make a simultaneous analysis of the high- and low-P_T parts of the charged-hadron P_T spectrum measured by the ALICE collaboration in central Pb-Pb collisions at sqrt{s}=2.76 TeV at the Large Hadron Collider…
At high energy, the gluon distribution in nuclei reaches large densities and eventually saturates due to recombinations, that play an important role in heavy ion collisions at RHIC and the LHC. The Color Glass Condensate provides a…
To have a better understanding of the matter formed in ultra-relativistic collisions, using the Color String Percolation Model (CSPM), we have estimated initial energy density, mean free path, squared speed of sound, shear viscosity to…
We consider principal component analysis (PCA) in decomposable Gaussian graphical models. We exploit the prior information in these models in order to distribute its computation. For this purpose, we reformulate the problem in the sparse…
In preparation for the heavy ion program at the relativistic heavy ion collider (RHIC) $d$-Au collisions were designated as a control experiment for possible discovery of a quark-gluon plasma (QGP) in more-central Au-Au collisions, and…
The rapid thermalization of quarks and gluons in the initial stages of relativistic heavy-ion collisions is treated using analytic solutions of a nonlinear diffusion equation with schematic initial conditions, and for gluons with boundary…