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Title:Inference of the Mass Composition of Cosmic Rays with Energies from 10[sup]18.5 to 10[sup]20 eV Using the Pierre Auger Observatory and Deep Learning
Authors:ID Abdul Halim, A. (Author)
ID Filipčič, Andrej (Author)
ID Lundquist, Jon Paul (Author)
ID Shivashankara, Shima Ujjani (Author)
ID Stanič, Samo (Author)
ID Vorobiov, Serguei (Author)
ID Zavrtanik, Danilo (Author)
ID Zavrtanik, Marko (Author), et al.
Files:.pdf PhysRevLett.134.021001.pdf (586,04 KB)
MD5: E644086DE91486629AB9BEBF7A4C484D
 
URL https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.134.021001
 
URL https://journals.aps.org/prl/pdf/10.1103/PhysRevLett.134.021001
 
Language:English
Work type:Not categorized
Typology:1.01 - Original Scientific Article
Organization:UNG - University of Nova Gorica
Abstract:We present measurements of the atmospheric depth of the shower maximum Xmax, inferred for the first time on an event-by-event level using the Surface Detector of the Pierre Auger Observatory. Using deep learning, we were able to extend measurements of the Xmax distributions up to energies of 100 EeV (10[sup]20 eV), not yet revealed by current measurements, providing new insights into the mass composition of cosmic rays at extreme energies. Gaining a 10-fold increase in statistics compared to the Fluorescence Detector data, we find evidence that the rate of change of the average Xmax with the logarithm of energy features three breaks at 6.5 ± 0.6 (stat) ± 1 (sys) EeV, 11 ± 2 (stat) ± 1 (sys) EeV, and 31 ± 5 (stat) ± 3 (sys) EeV, in the vicinity to the three prominent features (ankle, instep, suppression) of the cosmic-ray flux. The energy evolution of the mean and standard deviation of the measured Xmax distributions indicates that the mass composition becomes increasingly heavier and purer, thus being incompatible with a large fraction of light nuclei between 50 EeV and 100 EeV.
Keywords:ultra-high-energy cosmic rays (UHECRs), extensive air showers, Pierre Auger Observatory, UHECR mass composition, depth of the shower maximum, fluorescence detector, surface detector, deep learning
Publication status:Published
Publication version:Version of Record
Year of publishing:2025
Number of pages:10
Numbering:2025, 134
PID:20.500.12556/RUNG-9799 New window
COBISS.SI-ID:223002883 New window
DOI:10.1103/PhysRevLett.134.021001 New window
NUK URN:URN:SI:UNG:REP:YZXJQBH3
Publication date in RUNG:20.01.2025
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Record is a part of a journal

Title:PHYSICAL REVIEW LETTERS
Year of publishing:2025

Document is financed by a project

Funder:ARIS - Slovenian Research and Innovation Agency
Project number:P1-0031
Name:Večglasniška astrofizika

Funder:ARIS - Slovenian Research and Innovation Agency
Project number:P1-0385
Name:Daljinsko zaznavanje atmosferskih lastnosti

Funder:ARIS - Slovenian Research and Innovation Agency
Project number:I0-0033
Name:Infrastrukturni program Univerze v Novi Gorici

Funder:ARIS - Slovenian Research and Innovation Agency
Project number:N1-0111
Name:Identifikacija izvorov kozmičnih žarkov med aktivnimi galaksijami s curki

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

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