On Optimally Selecting Candidate Detectors with High Predicted Radio Signals from Energetic Cosmic Ray-Induced Extensive Air Showers

Monte Carlo simulations of induced extensive air showers (EASs) by ultra-high-energy cosmic rays are widely used in comparison with measured events at experiments to estimate the main cosmic ray characteristics, such as mass, energy, and arrival direction. However, these simulations are computationa...

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Main Authors: Tudor Alexandru Calafeteanu, Paula Gina Isar, Emil Ioan Slușanschi
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Universe
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Online Access:https://www.mdpi.com/2218-1997/11/6/192
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author Tudor Alexandru Calafeteanu
Paula Gina Isar
Emil Ioan Slușanschi
author_facet Tudor Alexandru Calafeteanu
Paula Gina Isar
Emil Ioan Slușanschi
author_sort Tudor Alexandru Calafeteanu
collection DOAJ
description Monte Carlo simulations of induced extensive air showers (EASs) by ultra-high-energy cosmic rays are widely used in comparison with measured events at experiments to estimate the main cosmic ray characteristics, such as mass, energy, and arrival direction. However, these simulations are computationally expensive, with running time scaling proportionally with the number of radio antennas included. The AugerPrime upgrade of the Pierre Auger Observatory will feature an array of 1660 radio antennas. As a result, simulating a single EAS using the full detector array will take weeks on a single CPU thread. To reduce the simulation time, detectors are commonly pre-selected based on their proximity to the shower core, using a selection ellipse based on the Cherenkov radiation footprint scaled by a fixed constant factor. While effective, this approach often includes many noisy antennas at high zenith angles, reducing computational efficiency. In this paper, we introduce an optimal method for selecting candidate detectors with high predicted signal-to-noise ratio for proton and iron primary cosmic rays, replacing the constant scaling factor with a function of the zenith angle. This approach significantly reduces simulation time—by more than 50% per CPU thread for the heaviest, most inclined showers—without compromising signal quality.
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spelling doaj-art-208f818fdc2b4eae95244c8efa9838f92025-08-20T02:21:54ZengMDPI AGUniverse2218-19972025-06-0111619210.3390/universe11060192On Optimally Selecting Candidate Detectors with High Predicted Radio Signals from Energetic Cosmic Ray-Induced Extensive Air ShowersTudor Alexandru Calafeteanu0Paula Gina Isar1Emil Ioan Slușanschi2Institute of Space Science—Subsidiary of INFLPR, 077125 Bucharest-Magurele, RomaniaInstitute of Space Science—Subsidiary of INFLPR, 077125 Bucharest-Magurele, RomaniaFaculty of Automatic Control and Computer Science, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, RomaniaMonte Carlo simulations of induced extensive air showers (EASs) by ultra-high-energy cosmic rays are widely used in comparison with measured events at experiments to estimate the main cosmic ray characteristics, such as mass, energy, and arrival direction. However, these simulations are computationally expensive, with running time scaling proportionally with the number of radio antennas included. The AugerPrime upgrade of the Pierre Auger Observatory will feature an array of 1660 radio antennas. As a result, simulating a single EAS using the full detector array will take weeks on a single CPU thread. To reduce the simulation time, detectors are commonly pre-selected based on their proximity to the shower core, using a selection ellipse based on the Cherenkov radiation footprint scaled by a fixed constant factor. While effective, this approach often includes many noisy antennas at high zenith angles, reducing computational efficiency. In this paper, we introduce an optimal method for selecting candidate detectors with high predicted signal-to-noise ratio for proton and iron primary cosmic rays, replacing the constant scaling factor with a function of the zenith angle. This approach significantly reduces simulation time—by more than 50% per CPU thread for the heaviest, most inclined showers—without compromising signal quality.https://www.mdpi.com/2218-1997/11/6/192cosmic raysair showersradio detectionMonte Carlo simulations
spellingShingle Tudor Alexandru Calafeteanu
Paula Gina Isar
Emil Ioan Slușanschi
On Optimally Selecting Candidate Detectors with High Predicted Radio Signals from Energetic Cosmic Ray-Induced Extensive Air Showers
Universe
cosmic rays
air showers
radio detection
Monte Carlo simulations
title On Optimally Selecting Candidate Detectors with High Predicted Radio Signals from Energetic Cosmic Ray-Induced Extensive Air Showers
title_full On Optimally Selecting Candidate Detectors with High Predicted Radio Signals from Energetic Cosmic Ray-Induced Extensive Air Showers
title_fullStr On Optimally Selecting Candidate Detectors with High Predicted Radio Signals from Energetic Cosmic Ray-Induced Extensive Air Showers
title_full_unstemmed On Optimally Selecting Candidate Detectors with High Predicted Radio Signals from Energetic Cosmic Ray-Induced Extensive Air Showers
title_short On Optimally Selecting Candidate Detectors with High Predicted Radio Signals from Energetic Cosmic Ray-Induced Extensive Air Showers
title_sort on optimally selecting candidate detectors with high predicted radio signals from energetic cosmic ray induced extensive air showers
topic cosmic rays
air showers
radio detection
Monte Carlo simulations
url https://www.mdpi.com/2218-1997/11/6/192
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AT emilioanslusanschi onoptimallyselectingcandidatedetectorswithhighpredictedradiosignalsfromenergeticcosmicrayinducedextensiveairshowers