TY - JOUR
T1 - Performance of a proposed event-type based analysis for the Cherenkov Telescope Array
AU - the CTA Consortium
AU - Hassan, T.
AU - Abdalla, H.
AU - Abe, H.
AU - Abe, S.
AU - Abusleme, A.
AU - Acero, F.
AU - Acharyya, A.
AU - Acín Portella, V.
AU - Ackley, K.
AU - Adam, R.
AU - Adams, C.
AU - Adhikari, S. S.
AU - Aguado-Ruesga, I.
AU - Agudo, I.
AU - Aguilera, R.
AU - Aguirre-Santaella, A.
AU - Aharonian, F.
AU - Alberdi, A.
AU - Alfaro, R.
AU - Alfaro, J.
AU - Alispach, C.
AU - Aloisio, R.
AU - Alves Batista, R.
AU - Amans, J. P.
AU - Amati, L.
AU - Amato, E.
AU - Ambrogi, L.
AU - Ambrosi, G.
AU - Ambrosio, M.
AU - Ammendola, R.
AU - Anderson, J.
AU - Anduze, M.
AU - Angüner, E. O.
AU - Antonelli, L. A.
AU - Antonuccio, V.
AU - Antoranz, P.
AU - Anutarawiramkul, R.
AU - Aragunde Gutierrez, J.
AU - Aramo, C.
AU - Araudo, A.
AU - Araya, M.
AU - Arbet-Engels, A.
AU - Arcaro, C.
AU - Arendt, V.
AU - Armand, C.
AU - Armstrong, T.
AU - Arqueros, F.
AU - Chaty, S.
AU - Fegan, S.
AU - Horan, D.
N1 - Publisher Copyright:
© Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)
PY - 2022/3/18
Y1 - 2022/3/18
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85145022346
M3 - Conference article
AN - SCOPUS:85145022346
SN - 1824-8039
VL - 395
JO - Proceedings of Science
JF - Proceedings of Science
M1 - 752
T2 - 37th International Cosmic Ray Conference, ICRC 2021
Y2 - 12 July 2021 through 23 July 2021
ER -