Performance of a proposed event-type based analysis for CTA

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  • uploaded June 25, 2021

Discussion timeslot (ZOOM-Meeting): 16. July 2021 - 18:00
ZOOM-Meeting URL: https://icrc2021.desy.de/pf_access_abstracts
Corresponding Session: https://icrc2021-venue.desy.de/channel/Presenter-Forum-1-Evening-All-Categories/48
Abstract:
'The Cherenkov Telescope Array (CTA) will be the next-generation observatory in the very-high-energy (20 GeV to 300 TeV) gamma-ray astroparticle physics field. 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 with respect to the standard procedure.'

Authors: Tarek Hassan Collado | Orel Gueta | Gernot Maier | Maximilian Nöthe | Michele Peresano | Ievgen Vovk | for the CTA Consortium
Collaboration: CTA

Indico-ID: 1143
Proceeding URL: https://pos.sissa.it/395/752

Tags:
Presenter: Tarek Hassan Collado

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