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Naslov: Application of machine learning techniques for cosmic ray event classification and implementation of a real-time ultra-high energy photon search with the surface detector of the Pierre Auger Observatory : dissertation Avtorji: ID Zehrer, Lukas (Avtor) ID Vorobiov, Serguei (Mentor) Več o mentorju... Datoteke: http://repozitorij.ung.si/IzpisGradiva.php?id=6815   thesis.pdf (46,18 MB) MD5: D294777E0EB2AFBD1F0CF04A40E85203   Jezik: Angleški jezik Vrsta gradiva: Doktorsko delo/naloga Tipologija: 2.08 - Doktorska disertacija Organizacija: FPŠ - Fakulteta za podiplomski študij Opis: 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. Ključne besede: 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 Kraj izida: Nova Gorica Kraj izvedbe: Nova Gorica Založnik: L. Zehrer Leto izida: 2021 Leto izvedbe: 2021 Št. strani: VII, 202 str. PID: 20.500.12556/RUNG-6815  COBISS.SI-ID: 82554371  UDK: 53 NUK URN: URN:SI:UNG:REP:ACGHVPCB Datum objave v RUNG: 27.10.2021 Število ogledov: 4504 Število prenosov: 223 Metapodatki:    Citiraj gradivo Navadno besedilo BibTeX EndNote XML EndNote/Refer RIS ABNT ACM Ref AMA APA Chicago 17th Author-Date Harvard IEEE ISO 690 MLA Vancouver : ZEHRER, Lukas, 2021, Application of machine learning techniques for cosmic ray event classification and implementation of a real-time ultra-high energy photon search with the surface detector of the Pierre Auger Observatory : dissertation [na spletu]. Doktorska disertacija. Nova Gorica : L. Zehrer. [Dostopano 28 april 2025]. Pridobljeno s: https://repozitorij.ung.si/IzpisGradiva.php?lang=slv&id=6815 Kopiraj citat

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Sekundarni jezik

Jezik:	Slovenski jezik
Naslov:	Razvoj metod strojnega učenja za identifikacijo kozmičnih delcev ekstremnih energij ter njihova implementacija pri iskanju fotonov ekstremnih energij s površinskimi detektorji Observatorija Pierre Auger

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