Performance update of an event-type based analysis for the Cherenkov Telescope Array



Bernete Medrano, Juan, Gueta, Orel, Hassan, Tarek, Linhoff, Maximilian, Maier, Gernot, Sinha, Atreyee, Abe, Kazuki, Abe, Shotaro, Acharyya, Atreya, Adam, Remi
et al (show 554 more authors) (2023) Performance update of an event-type based analysis for the Cherenkov Telescope Array. In: 38th International Cosmic Ray Conference, 2023-7-26 - ?.

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Abstract

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.

Item Type: Conference or Workshop Item (Unspecified)
Divisions: Faculty of Science and Engineering > School of Physical Sciences
Depositing User: Symplectic Admin
Date Deposited: 16 Mar 2023 09:07
Last Modified: 24 Mar 2024 20:08
DOI: 10.22323/1.444.0738
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3169101