Learning the composition of ultrahigh energy cosmic rays

Bortolato, Blaž (author), Kamenik, Jernej (author), Tammaro, Michele (author)

We apply statistical inference on the Pierre Auger Open Data to discern the mass composition of cosmic rays at different energies. Working with longitudinal electromagnetic profiles of cosmic ray showers, in particular their peaking depths X$_{max}$, we employ central moments of the X$_{max}$ distributions to discriminate between different shower compositions. We find that already the first few moments entail the most relevant information to infer the primary cosmic ray mass spectrum. Our approach, based on an unbinned likelihood, allows us to consistently account for sources of statistical uncertainties due to finite datasets, both measured and simulated, as well as systematic effects. Finally, we provide a quantitative comparison of different high energy hadronic interaction models available in the atmospheric shower simulation codes.

• Language: English
• Keywords: cosmic rays, astroparticles, detectors, gravitation, cosmology, astrophysics, statistical physics, thermodynamics
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FMF - Faculty of Mathematics and Physics
• Publication status: Published
• Publication version: Version of Record
• Year: 2023
• Number of pages: 32 str.
• Numbering: Vol. 108, iss. 2, art. 022004
• PID: 20.500.12556/RUL-164666
• UDC: 53
• ISSN on article: 2470-0010
• DOI: 10.1103/PhysRevD.108.022004
• COBISS.SI-ID: 159693827
• Publication date in RUL: 06.11.2024

Record is a part of a journal

• Title: Physical review
• Shortened title: Phys. rev., D
• Publisher: American Physical Society
• ISSN: 2470-0010
• COBISS.SI-ID: 29757223

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.

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: J1-3013
• Name: Precizne študije okusov s pomočjo strojnega učenja

• Funder: ARRS - Slovenian Research Agency
• Project number: P1-0035
• Name: Teorija jedra, osnovnih delcev in polj