Related papers: Cosmic-Ray Mass Composition around the Knee via Pr…

Measurements of the lateral distribution of Cherenkov photons with the wide-angle atmospheric Cherenkov light detector array AIROBICC and of the charged particle lateral distribution with the scintillator matrix of the HEGRA air-shower…

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…

Analysis of the experimental data on cosmic ray spectra in the framework of the proposed model with two types of sources leads to conclusion, that sources with particle generation spectral exponent $p\sim 2.85$ give the major contribution…

We introduce a Principal Component Analysis (PCA)--Bayesian framework for extracting chemical freeze-out conditions in relativistic heavy-ion collisions that resolves long-standing ambiguities in hadron-ratio--based analyses. By…

This work establishes oblique shocks in Massive Star Clusters (MSC) as a primary mechanism for accelerating cosmic rays (CR) up to the knee of the energy spectrum. We develop a model that incorporates the combined contribution of supernova…

Principal component analysis (PCA) is a standard dimensionality reduction technique used in various research and applied fields. From an algorithmic point of view, classical PCA can be formulated in terms of operations on a multivariate…

We conduct a review of experimental results on Ultra-High Energy Cosmic Rays (UHECR's) including measurements of the features of the spectrum, the composition of the primary particle flux and the search for anisotropy in event arrival…

The observation of a small change in spectral slope, or 'knee' in the fluxes of cosmic rays near energies 10^15 eV has caused much speculation since its discovery over 40 years ago. The origin of this feature remains unknown. A small…

Often the relation between the variables constituting a multivariate data space might be characterized by one or more of the terms: ``nonlinear'', ``branched'', ``disconnected'', ``bended'', ``curved'', ``heterogeneous'', or, more general,…

Methodologies for multidimensionality reduction aim at discovering low-dimensional manifolds where data ranges. Principal Component Analysis (PCA) is very effective if data have linear structure. But fails in identifying a possible…

We present a method using principal component analysis (PCA) to process x-ray pulses with severe shape variation where traditional optimal filter methods fail. We demonstrate that PCA is able to noise-filter and extract energy information…

Principal component analysis (PCA) is a dimensionality reduction method in data analysis that involves diagonalizing the covariance matrix of the dataset. Recently, quantum algorithms have been formulated for PCA based on diagonalizing a…

Investigations of the energy spectrum as well as the mass composition of cosmic rays in the energy range of PeVto EeV are important for understanding both, the origin of the galactic and the extragalactic cosmic rays. Recently, three modern…

Principal Component Analysis (PCA) and its nonlinear extension Kernel PCA (KPCA) are widely used across science and industry for data analysis and dimensionality reduction. Modern deep learning tools have achieved great empirical success,…

In this paper, we propose a novel robust Principal Component Analysis (PCA) for high-dimensional data in the presence of various heterogeneities, especially the heavy-tailedness and outliers. A transformation motivated by the characteristic…

In this paper, we study the problem of computing a Principal Component Analysis of data affected by Poisson noise. We assume samples are drawn from independent Poisson distributions. We want to estimate principle components of a fixed…

First preliminary results of the balloon-borne experiment SPHERE-2 on the all-nuclei primary cosmic rays (PCR) spectrum and primary composition are presented. The primary spectrum in the energy range $10^{16}$--$5\cdot10^{17}$ eV was…

We study the problem of high-dimensional Principal Component Analysis (PCA) with missing observations. In simple, homogeneous missingness settings with a noise level of constant order, we show that an existing inverse-probability weighted…

Recent results of advanced experiments with sophisticated measurements of cosmic rays in the energy range of the so called knee at a few PeV indicate a distinct knee in the energy spectra of light primary cosmic rays and an increasing…

Principal component analysis has been widely adopted to reduce the dimension of data while preserving the information. The quantum version of PCA (qPCA) can be used to analyze an unknown low-rank density matrix by rapidly revealing the…