Related papers: Estimation of initial state structures in high ene…
Principal Component Analysis (PCA) and K-means constitute fundamental techniques in multivariate analysis. Although they are frequently applied independently or sequentially to cluster observations, the relationship between them, especially…
This paper presents a novel method for the reconstruction of interaction vertices in particle collision data. The algorithm is an agglomerative clustering technique designed for high-luminosity environments in current and future…
The PYTHIA Monte Carlo (PMC), first introduced more than thirty years ago, remains a popular simulation tool both for analysis of $p$-$p$ collision dynamics and for detector design and calibration. The PMC assumes that almost all produced…
Principal Component Analysis (PCA) finds a linear mapping and maximizes the variance of the data which makes PCA sensitive to outliers and may cause wrong eigendirection. In this paper, we propose techniques to solve this problem; we use…
We apply principal component analysis, a method frequently used in image processing and unsupervised machine learning, to characterize particle displacements observed in the steady shear flow of amorphous solids. PCA produces a…
This is a review of the parton cascade model (PCM) which provides a QCD-based description of nucleus-nucleus reactions at very high energy. The PCM describes the collision dynamics within the early and dense phase of the reaction in terms…
Multiplicity fluctuations in heavy ion collisions obtain comparable contributions both from initial stage of the collisions, and from final stage interaction. We calculate the former component, using the ``wounded nucleon'' model and…
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…
In high-energy heavy-ion collisions, the initial condition of the produced quark-gluon plasma (QGP) and its evolution are sensitive to collective nuclear structure parameters describing the shape and radial profiles of the nuclei. We find a…
Principal Component analysis (PCA) is a useful statistical technique that is commonly used for multivariate analysis of correlated variables. It is usually applied as a dimension reduction method: the top principal components (PCs)…
Principal components analysis (PCA) is a widely used dimension reduction technique with an extensive range of applications. In this paper, an online distributed algorithm is proposed for recovering the principal eigenspaces. We further…
A multi-phase transport (AMPT) model has been successful in explaining a wide range of observables in relativistic heavy ion collisions. In this work, we implement a modern set of free proton parton distribution functions and an impact…
We take hydrodynamic initial conditions in relativistic heavy ion collisions from the Color Glass Condensate (CGC) picture through the kT factorization formula. Gluon distributions produced from the CGC are found to provide good initial…
We study distributed principal component analysis (PCA) in high-dimensional settings under the spiked model. In such regimes, sample eigenvectors can deviate significantly from population ones, introducing a persistent bias. Existing…
Identified-hadron (PID) spectra from 2.76 TeV Pb-Pb and $p$-$p$ collisions are analyzed via a two-component (soft + hard) model (TCM) of hadron production in high-energy nuclear collisions. The Pb-Pb TCM is adopted with minor changes from a…
We propose a spectral clustering method based on local principal components analysis (PCA). After performing local PCA in selected neighborhoods, the algorithm builds a nearest neighbor graph weighted according to a discrepancy between the…
Modeling of the process of the formation of nuclear clusters in the hot nuclear matter is a challenging task. We present the novel n-body dynamical transport approach - PHQMD (Parton-Hadron-Quantum-Molecular Dynamics) [1] for the…
We report on recent progress in understanding multiparticle correlations in small systems from the initial state. First, we consider a proof-of-principle parton model, which we use to demonstrate that many of the multiparticle correlations…
We study the nuclear stopping in high energy nuclear collisions using the constituent quark model. It is assumed that wounded nucleons with different number of interacted quarks hadronize in different ways. The probabilities of having such…
The space-time picture of hadron formation in high-energy collisions with nuclear targets is still poorly known. The tests of hadron formation was suggested for the first stage of SPD running. They will require measuring charged pion and…