相关论文: Neural Networks as a Composition Diagnostic for Ul…
The output parameters from the ground array of the Auger South observatory, were simulated for the typical instrumental and environmental conditions at its Malarg\"ue site using the code sample-sim. Extensive air showers started by photons,…
The origin and nature of ultra high energy cosmic rays remains being a mystery. However, great progress has been made in recent years due to the observations performed by the Pierre Auger Observatory and Telescope Array. In particular, it…
We apply statistical inference on the Pierre Auger Open Data to discern for the first time the full mass composition of cosmic rays at different energies. Working with longitudinal electromagnetic profiles of cosmic ray showers, in…
Using a combination of a preshower detector and a charged particle veto, it is shown that the neural network method is able to provide satisfactory discrimination between photons and hadrons in the case of extremely high particle density…
The Fluorescence Detector of the Pierre Auger Observatory measures the atmospheric depth, $X_{max}$, where the longitudinal profile of the high energy air showers reaches its maximum. This is sensitive to the nuclear mass composition of the…
Presently large systematic uncertainties remain in the description of hadronic interactions at ultra-high energies and a fully consistent description of air-shower experimental data is yet to be reached. The amount of data collected by the…
The combination of fluorescence and surface detectors at the Pierre Auger Observatory offers unprecedented precision in testing models of hadronic interactions at center-of-mass energies around 70 TeV and beyond. However, for some time,…
Using the data taken at the Pierre Auger Observatory between December 2004 and December 2012, we have examined the implications of the distributions of depths of atmospheric shower maximum (Xmax), using a hybrid technique, for composition…
The hybrid design of the Pierre Auger Observatory allows for the measurement of a number of properties of extensive air showers initiated by ultra-high energy cosmic rays. By comparing these measurements to predictions from air shower…
We study the implications of Ultra-High Energy Cosmic Ray (UHECR) data from the Pierre Auger Observatory for potential accelerator candidates and cosmogenic neutrino fluxes for different combinations of nuclear disintegration and air-shower…
Precision phenomenological studies of high-multiplicity scattering processes at collider experiments present a substantial theoretical challenge and are vitally important ingredients in experimental measurements. Machine learning technology…
Using the Auger mass-composition analysis of ultra high energy cosmic rays, based on the shape-fitting of $X_{max}$ distributions, we demonstrate that mass composition and energy spectra measured by Auger, Telescope Array and HiRes can be…
The Pierre Auger Observatory, the world's largest observatory of ultra-high-energy cosmic rays (UHECR), offers a unique insight into the properties of hadronic interactions occurring in air showers at energies well above those reached at…
We explore the feasibility of estimating primary cosmic ray composition at ultra high energies from the sum of muon, electron and photon densities and the depth of maximum of extensive air showers detected by the Auger Observatory. From the…
The Pierre Auger Observatory in Argentina provides the largest data sample of the cosmic ray events with energy above 10$^{18}$ eV. These high energy events can be used to test our understanding of the hadronic interactions at energies…
In this study, we employ a conventional deep neural network (NN) framework integrated with physics-based constraints to predict charged hadron multiplicity ($N_{\text{ch}}$) in heavy-ion collisions. The goal is to assess the performance of…
In this paper we infer the mass composition of the ultra high energy cosmic rays (UHECRs) from measurements of $X_{\rm max}$ distributions recorded at the Pierre Auger (2014) and Telescope Array (TA) (2016) Observatories, by fitting them…
Deep neural networks are a powerful technique that have found ample applications in several branches of Physics. In this work, we apply machine learning algorithms to a specific problem of Cosmic Ray Physics: the estimation of the muon…
Ultra-high-energy photons have long been sought as tracers of the most energetic processes in the Universe. Several sources can contribute to a diffuse photon flux, including interactions of cosmic rays with Galactic matter and radiation…
The mass composition of ultra high-energy cosmic ray (UHECRs) can be inferred from measurements of $X_{\rm max}$ distributions by fitting them with Monte Carlo (MC) predictions for different primary species of nuclei in each energy…