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Principal component analysis (PCA) is one of the most popular dimension reduction techniques in statistics and is especially powerful when a multivariate distribution is concentrated near a lower-dimensional subspace. Multivariate extreme…
The mass ordering of mean transverse momentum $\left<p_T\right>$ and of the Fourier harmonic coefficient $v_2 (p_T)$ of azimuthally anisotropic particle distributions in high energy hadron collisions is often interpreted as evidence for the…
Principal Component Analysis (PCA) is a cornerstone of dimensionality reduction, yet its classical formulation relies critically on second-order moments and is therefore fragile in the presence of heavy-tailed data and impulsive noise.…
The first order behavior of multivariate heavy-tailed random vectors above large radial thresholds is ruled by a limit measure in a regular variation framework. For a high dimensional vector, a reasonable assumption is that the support of…
A very important issue concerning protostellar jets is the mechanism behind their formation. Obtaining information on the region at the base of a jet can shed light into the subject and some years ago this has been done through a search for…
In this letter we study the photoproduction of heavy quarks and vector mesons in the coherent proton-nucleus (pA) interactions for RHIC and LHC energies and analyze if these processes can be used to determine the QCD dynamics at high…
I will present an overview of identified particle spectra at high $p_T$ ($p_T{}^{>}_{\sim}$ 5 GeV/$c$) in both p+p collisions and AA collisions at RHIC. In p+p collisions, summary of particle ratios of K, $\eta$, $\omega$, $\rho$, $\phi$,…
$\alpha$-clustered structures in light nuclei could be studied through "snapshots" taken by relativistic heavy-ion collisions. A multiphase transport (AMPT) model is employed to simulate the initial structure of collision nuclei and the…
We outline how principal component analysis (PCA) can be applied to particle configuration data to detect a variety of phase transitions in off-lattice systems, both in and out of equilibrium. Specifically, we discuss its application to…
Principal Component Analysis (PCA) is a ubiquitous tool with many applications in machine learning including feature construction, subspace embedding, and outlier detection. In this paper, we present an algorithm for computing the top…
We calculate the hadron spectra in high-energy pp, pA and AA collisions within a pQCD parton model. Analyses of experimental data show that the modification of the spectra in pA and AA as compared to that in $pp$ collisions is consistent…
Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which better exploits the complicated spatial structure of high-dimensional features.…
As tensors become widespread in modern data analysis, Tucker low-rank Principal Component Analysis (PCA) has become essential for dimensionality reduction and structural discovery in tensor datasets. Motivated by the common scenario where…
Principal Component Analysis (PCA) is a workhorse of modern data science. While PCA assumes the data conforms to Euclidean geometry, for specific data types, such as hierarchical and cyclic data structures, other spaces are more…
A Monte Carlo model, initially developed for soft pp and AA collisions at high energy, is applied for proton-lead interaction at the LHC energy. Elementary collisions are implemented at the partonic level and do not involve the usual…
Principal Component Analysis (PCA) is a very successful dimensionality reduction technique, widely used in predictive modeling. A key factor in its widespread use in this domain is the fact that the projection of a dataset onto its first…
A computational approach by an implementation of the Principle Component Analysis (PCA) with K-means and Gaussian Mixture (GM) clustering methods from Machine Learning (ML) algorithms to identify structural and dynamical heterogeneities of…
Soft processes in pp and AA interactions are considered in the framework of phenomenological model with color strings formation and fusion. Elementary parton collisions are realized in the model as the interaction of two colour dipoles from…
We investigate the initial state geometric quantities of heavy ion collisions based on the quark participant assumption in the Glauber multiple scattering approach. A systematic comparison to the nucleon participant assumption has been…
Traditional load analysis is facing challenges with the new electricity usage patterns due to demand response as well as increasing deployment of distributed generations, including photovoltaics (PV), electric vehicles (EV), and energy…