Performance of a proposed event-type based analysis for the Cherenkov Telescope Array

the CTA Consortium

Performance of a proposed event-type based analysis for the Cherenkov Telescope Array

the CTA Consortium

Physics

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Research output: Contribution to journal › Conference article › peer-review

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.

Original language	English
Article number	752
Journal	Proceedings of Science
Volume	395
Publication status	Published - 18 Mar 2022
Event	37th International Cosmic Ray Conference, ICRC 2021 - Virtual, Berlin, Germany Duration: 12 Jul 2021 → 23 Jul 2021

ASJC Scopus subject areas

Multidisciplinary

Cite this

@article{deb7fb046c2c40da9becc16470c986fa,

title = "Performance of a proposed event-type based analysis for the Cherenkov Telescope Array",

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.",

author = "\{the CTA Consortium\} and T. Hassan and H. Abdalla and H. Abe and S. Abe and A. Abusleme and F. Acero and A. Acharyya and \{Ac{\'i}n Portella\}, V. and K. Ackley and R. Adam and C. Adams and Adhikari, \{S. S.\} and I. Aguado-Ruesga and I. Agudo and R. Aguilera and A. Aguirre-Santaella and F. Aharonian and A. Alberdi and R. Alfaro and J. Alfaro and C. Alispach and R. Aloisio and \{Alves Batista\}, R. and Amans, \{J. P.\} and L. Amati and E. Amato and L. Ambrogi and G. Ambrosi and M. Ambrosio and R. Ammendola and J. Anderson and M. Anduze and Ang{\"u}ner, \{E. O.\} and Antonelli, \{L. A.\} and V. Antonuccio and P. Antoranz and R. Anutarawiramkul and \{Aragunde Gutierrez\}, J. and C. Aramo and A. Araudo and M. Araya and A. Arbet-Engels and C. Arcaro and V. Arendt and C. Armand and T. Armstrong and F. Arqueros and L. Arrabito and M. Barnard and S. Razzaque",

note = "Publisher Copyright: {\textcopyright} 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); 37th International Cosmic Ray Conference, ICRC 2021 ; Conference date: 12-07-2021 Through 23-07-2021",

year = "2022",

month = mar,

day = "18",

language = "English",

volume = "395",

journal = "Proceedings of Science",

issn = "1824-8039",

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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 - Arrabito, L.

AU - Barnard, M.

AU - Razzaque, S.

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 -

Performance of a proposed event-type based analysis for the Cherenkov Telescope Array

Abstract

ASJC Scopus subject areas

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