| 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 |
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| 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. |
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| Files: |  PhysRevLett.134.021001.pdf (586,04 KB)
MD5: E644086DE91486629AB9BEBF7A4C484D
 https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.134.021001
https://journals.aps.org/prl/pdf/10.1103/PhysRevLett.134.021001
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| Language: | English |
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| Work type: | Not categorized |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | UNG - University of Nova Gorica
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| 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. |
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| 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 |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Year of publishing: | 2025 |
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| Number of pages: | 10 |
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| Numbering: | 2025, 134 |
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| PID: | 20.500.12556/RUNG-9799  |
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| COBISS.SI-ID: | 223002883 |
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| DOI: | 10.1103/PhysRevLett.134.021001 |
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| NUK URN: | URN:SI:UNG:REP:YZXJQBH3 |
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| Publication date in RUNG: | 20.01.2025 |
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| Views: | 55 |
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| Downloads: | 0 |
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