dissertation
Lukas Zehrer (Author), Serguei Vorobiov (Mentor)

Abstract

Despite their discovery already more than a century ago, Cosmic Rays (CRs) still did not divulge all their properties yet. Theories about the origin of ultra-high energy (UHE, > 10^18 eV) CRs predict accompanying primary photons. The existence of UHE photons can be investigated with the world’s largest ground-based experiment for detection of CR-induced extensive air showers (EAS), the Pierre Auger Observatory, which offers an unprecedented exposure to rare UHE cosmic particles. The discovery of photons in the UHE regime would open a new observational window to the Universe, improve our understanding of the origin of CRs, and potentially uncloak new physics beyond the standard model. The novelty of the presented work is the development of a "real-time" photon candidate event stream to a global network of observatories, the Astrophysical Multimessenger Observatory Network (AMON). The stream classifies CR events observed by the Auger surface detector (SD) array as regards their probability to be photon nominees, by feeding to advanced machine learning (ML) methods observational air shower parameters of individual CR events combined in a multivariate analysis (MVA). The described straightforward classification procedure further increases the Pierre Auger Observatory’s endeavour to contribute to the global effort of multi-messenger (MM) studies of the highest energy astrophysical phenomena, by supplying AMON partner observatories the possibility to follow-up detected UHE events, live or in their archival data.

Keywords

astroparticle physics;ultra-high energy cosmic rays;ultra-high energy photons;extensive air showers;Pierre Auger Observatory;multi-messenger;AMON;machine learning;multivariate analysis;dissertations;

Data

Language: English
Year of publishing:
Typology: 2.08 - Doctoral Dissertation
Organization: UNG FPŠ - Graduate School
Publisher: [L. Zehrer]
UDC: 539.1(043.3)
COBISS: 82554371 Link will open in a new window
Views: 1952
Downloads: 130
Average score: 0 (0 votes)
Metadata: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Other data

Secondary language: Slovenian
Secondary title: Razvoj metod strojnega učenja za identifikacijo kozmičnih delcev ekstremnih energij ter njihova implementacija pri iskanju fotonov ekstremnih energij s površinskimi detektorji Observatorija Pierre Auger
Secondary keywords: astrofizika osnovnih delcev;visokoenergijski kozmični žarki;visokoenergijski fotoni;obdelava podatkov;strojno učenje;veliki detektorski sistemi;Observatorij Pierre Auger;disertacije;Astrofizika;Disertacije;
URN: URN:SI:UNG
Type (COBISS): Doctoral dissertation
Thesis comment: Univ. v Novi Gorici, Fak. za podiplomski študij
Pages: VII, 202 str.
ID: 13582500