T. Hassan (Avtor), Saptashwa Bhattacharyya (Avtor), B. Marčun (Avtor), Judit Pérez Romero (Avtor), Samo Stanič (Avtor), Veronika Vodeb (Avtor), Serguei Vorobiov (Avtor), Gabrijela Zaharijas (Avtor), Marko Zavrtanik (Avtor), Danilo Zavrtanik (Avtor), Miha Živec (Avtor)

Povzetek

The Cherenkov Telescope Array (CTA) will be the next-generation observatory in the field of very-high-energy (20 GeV to 300 TeV) gamma-ray astroparticle physics. Classically, data analysis in the field maximizes sensitivity by applying quality cuts on the data acquired. These cuts, optimized using Monte Carlo simulations, select higher quality events from the initial dataset. Subsequent steps of the analysis typically use the surviving events to calculate one set of instrument response functions (IRFs). An alternative approach is the use of event types, as implemented in experiments such as the Fermi-LAT. In this approach, events are divided into sub-samples based on their reconstruction quality, and a set of IRFs is calculated for each sub-sample. The sub-samples are then combined in a joint analysis, treating them as independent observations. This leads to an improvement in performance parameters such as sensitivity, angular and energy resolution. Data loss is reduced since lower quality events are included in the analysis as well, rather than discarded. In this study, machine learning methods will be used to classify events according to their expected angular reconstruction quality. We will report the impact on CTA high-level performance when applying such an event-type classification, compared to the classical procedure.

Ključne besede

very-high energy gamma-rays;Cherenkov Telescope Array;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.06 - Objavljeni znanstveni prispevek na konferenci (vabljeno predavanje)
Organizacija: UNG - Univerza v Novi Gorici
UDK: 539.1
COBISS: 164822019 Povezava se bo odprla v novem oknu
ISSN: 1824-8039
Št. ogledov: 94
Št. prenosov: 3
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Vrsta dela (COBISS): Delo ni kategorizirano
Strani: str. 1-15
Čas izdaje: 2022
ID: 19945506
Priporočena dela:
, ni podatka o podnaslovu
, ǂthe ǂworld’s largest VHE gamma-ray observatory
, layout, design and performance
, ǂa ǂpotential PeVatron candidate for the CTA in the northern hemisphere
, ni podatka o podnaslovu