Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
Energy spectra of elemental groups of cosmic rays with the KASCADE experiment data and machine learning. / Kuznetsov, M.Yu.; Petrov, N.; Plokhikh, I. и др.
в: Journal of Cosmology and Astroparticle Physics, Том 2024, № 5, 125, 01.05.2024.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
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TY - JOUR
T1 - Energy spectra of elemental groups of cosmic rays with the KASCADE experiment data and machine learning
AU - Kuznetsov, M.Yu.
AU - Petrov, N.
AU - Plokhikh, I.
AU - Sotnikov, V.
N1 - © 2024 IOP Publishing Ltd and Sissa Medialab.
PY - 2024/5/1
Y1 - 2024/5/1
N2 - We report the reconstruction of the mass component spectra of cosmic rays (protons, helium, carbon, silicon and iron) and their mean mass composition, at energies from 1.4 to 100 PeV. The results are derived from the archival data of the extensive air shower experiment KASCADE. We use a novel machine learning technique developed specifically for this reconstruction, and post-LHC hadronic interaction models: QGSJet-II.04, EPOS-LHC and Sibyll 2.3c. We have found an excess of the proton component and a deficit of intermediate and heavy nuclei components compared to the original KASCADE results. The spectra of protons and helium show a knee-like behavior at ∼ 4.4 PeV and ∼ 11 PeV, with significances 5.2σ and 3.9σ, respectively. The spectrum of the iron component has a hint (2.4σ) of a hardening at ∼ 4.5 PeV, which can be interpreted as a counterpart of a hardening in the proton spectrum at 166 TeV, recently reported by the GRAPES-3 experiment. The systematic uncertainties of our analysis were found to be smaller than those of the original KASCADE, as well as those of IceTop and TALE experiments, over the most part of the energy range studied. We also estimated separately the uncertainty related to the difference between the three mentioned hadronic interaction models. We also compute a mean logarithm mass of CR flux as a function of energy. It is in agreement with the results of IceTop, TALE and LHAASO within the uncertainties.
AB - We report the reconstruction of the mass component spectra of cosmic rays (protons, helium, carbon, silicon and iron) and their mean mass composition, at energies from 1.4 to 100 PeV. The results are derived from the archival data of the extensive air shower experiment KASCADE. We use a novel machine learning technique developed specifically for this reconstruction, and post-LHC hadronic interaction models: QGSJet-II.04, EPOS-LHC and Sibyll 2.3c. We have found an excess of the proton component and a deficit of intermediate and heavy nuclei components compared to the original KASCADE results. The spectra of protons and helium show a knee-like behavior at ∼ 4.4 PeV and ∼ 11 PeV, with significances 5.2σ and 3.9σ, respectively. The spectrum of the iron component has a hint (2.4σ) of a hardening at ∼ 4.5 PeV, which can be interpreted as a counterpart of a hardening in the proton spectrum at 166 TeV, recently reported by the GRAPES-3 experiment. The systematic uncertainties of our analysis were found to be smaller than those of the original KASCADE, as well as those of IceTop and TALE experiments, over the most part of the energy range studied. We also estimated separately the uncertainty related to the difference between the three mentioned hadronic interaction models. We also compute a mean logarithm mass of CR flux as a function of energy. It is in agreement with the results of IceTop, TALE and LHAASO within the uncertainties.
KW - Machine learning
KW - cosmic ray experiments
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85194864588&origin=inward&txGid=84c7c227efc593d7f9ce6442faceb64f
UR - https://www.mendeley.com/catalogue/14cfdedc-0096-33f4-95ad-37fe6883bded/
U2 - 10.1088/1475-7516/2024/05/125
DO - 10.1088/1475-7516/2024/05/125
M3 - Article
VL - 2024
JO - Journal of Cosmology and Astroparticle Physics
JF - Journal of Cosmology and Astroparticle Physics
SN - 1475-7516
IS - 5
M1 - 125
ER -
ID: 59996326