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Title: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
Authors:ID Zehrer, Lukas (Author)
ID Vorobiov, Serguei (Mentor) More about this mentor... New window
Files:.pdf thesis.pdf (46,18 MB)
MD5: D294777E0EB2AFBD1F0CF04A40E85203
 
URL http://repozitorij.ung.si/IzpisGradiva.php?id=6815
 
Language:English
Work type:Doctoral dissertation
Typology:2.08 - Doctoral Dissertation
Organization:FPŠ - Graduate School
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
Place of publishing:Nova Gorica
Place of performance:Nova Gorica
Publisher:L. Zehrer
Year of publishing:2021
Year of performance:2021
Number of pages:VII, 202 str.
PID:20.500.12556/RUNG-6815 New window
COBISS.SI-ID:82554371 New window
UDC:53
NUK URN:URN:SI:UNG:REP:ACGHVPCB
Publication date in RUNG:27.10.2021
Views:2946
Downloads:153
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Secondary language

Language:Slovenian
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


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