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

CTA consortium

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

CTA consortium

CIEMAT
German Electron Synchrotron
TU Dortmund University
Complutense University
Tokai University
The University of Tokyo
University of Alabama
Université Côte d'Azur
Laboratoire Leprince-Ringuet
University of Barcelona
CSIC - Institute of Astrophysics of Andalusia
Pontificia Universidad Católica de Chile
Universidad Autónoma de Madrid
PSL Research University
Osservatorio Astrofisico Di Arcetri, Florence
Osservatorio Astronomico Roma
Scientific and Technological Research Council of Turkey
National Institute for Nuclear Physics
Laboratoire Univers et Particules de Montpellier
University of Groningen
Universidade de São Paulo
University of Namibia
North West University
Monash University
University of Geneva
Universidad del Azuay
Centro Brasileiro de Pesquisas Físicas
University of Padua
University of Potsdam
University of the Witwatersrand
Ruhr University Bochum
Harvard-Smithsonian Center for Astrophysics
Max Planck Institute for Nuclear Physics
Max Planck Institute for Physics (Werner Heisenberg Institute)
NASU - Pidstryhach Institute for Applied Problems of Mechanics and Mathematics
Université Savoie Mont Blanc
University of Nova Gorica
University of Tübingen
Swiss Federal Institute of Technology Zurich
Polytechnic University of Bari
Institute for High Energy Physics
Czech Academy of Sciences
Sorbonne Université
Osservatorio Astronomico di Brera
University of Zagreb
Université Paris-Saclay
Adelaide University
Istituto di Astrofisica Spaziale e Fisica Cosmica di Bologna
Dublin Institute for Advanced Studies
Durham University
University of Udine
Armagh Observatory
University of New South Wales
Commissariat à l’énergie atomique et aux énergies alternatives
Cherenkov Telescope Array Observatory
Autonomous University of Barcelona
Kasetsart University
National Astronomical Research Institute of Thailand
National Institute for Astrophysics
Universidade Cruzeiro do Sul
Lund University
Centre de Physique des Particules de Marseille
Osservatorio Astronomico di Capodimonte
University of Alcalá
University of Bari
APC - AstroParticule et Cosmologie
Dublin City University
University of Turin
Universidade Federal do Paraná
Osservatorio Astrofisico di Catania
Universidad de Valparaíso
University of Wisconsin-Madison
University of Bergen
University of Naples Federico II
Universidad Técnica Federico Santa Maria
University of Würzburg
Friedrich-Alexander University Erlangen-Nürnberg
The University of Chicago
LAPTH Laboratoire d'Annecy-Le-Vieux de Physique Theorique
University of California at Santa Cruz
University of Pavia
Kyiv National Taras Shevchenko University
University of Manitoba
RIKEN
University of Perugia
Nagoya University
A. Alikhanian Yerevan Institute of Physics
Universidad Andrés Bello
Universidad Nacional Autónoma de México
Universidade Federal do Espírito Santo
Open University of Israel
George Washington University
University of Liverpool
King's College London
Yamagata University
Yamanashi Gakuin University
Université Paul Sabatier Toulouse III
University of Innsbruck
Joint Laboratory of Optics
University of Turku
Josip Juraj Strossmayer University of Osijek
Gran Sasso Science Institute
University of L'Aquila
University of Lodz
Jagiellonian University in Kraków
Heidelberg University
UJF-CNRS (UMR 5571)
University of Utah
Polish Academy of Sciences
High Energy Accelerator Research Organization, Institute of Particle and Nuclear Physics
University of Leicester
Western Sydney University
Université Bordeaux 1
University of Trieste
University of La Laguna
University of Jaén
University of Amsterdam
Homi Bhabha National Institute
University of Catania
University of Palermo
Comillas Pontifical University
Universidad de Chile
CSIC - Institute of Space Sciences
Institute of Nuclear Physics PAN
Konan University
Hiroshima University
Columbia University
Kitasato University
Kavli Institute for Particle Astrophysics and Cosmology
University of Białystok
Charles University
Ivan Franko National University of L'viv
University of California at Los Angeles
Tokushima University
Georgia Institute of Technology
University of Pisa
University of Rijeka
Ruder Boskovic Institute
Astronomical Observatory of Padua
University of Siena
University of Johannesburg
University of Oxford
Universidad Metropolitana de Ciencias de la Educación
Universidad de Concepción
University of Split
Observatoire de Sauverny
Humboldt University of Berlin
NASU - Main Astronomical Observatory
AGH University of Krakow
Nicolaus Copernicus University in Toruń
Cherenkov Telescope Array Observatory GGmbH
Warsaw University of Technology
Sun Yat-Sen University
Aoyama Gakuin University
Kyoto University
Port d'Informació Científica
Ibaraki University

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. The traditional approach to data analysis in this field is to apply quality cuts, optimized using Monte Carlo simulations, on the data acquired to maximize sensitivity. Subsequent steps of the analysis typically use the surviving events to calculate one set of instrument response functions (IRFs) to physically interpret the results. However, an alternative approach is the use of event types, as implemented in experiments such as the Fermi-LAT. This approach divides events 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. In previous works we demonstrated that event types, classified using Machine Learning methods according to their expected angular reconstruction quality, have the potential to significantly improve the CTA angular and energy resolution of a point-like source analysis. Now, we validated the production of event-type wise full-enclosure IRFs, ready to be used with science tools (such as Gammapy and ctools). We will report on the impact of using such an event-type classification on CTA high-level performance, compared to the traditional procedure.

Original language	English
Article number	738
Journal	Proceedings of Science
Volume	444
State	Published - 27 Sep 2024
Externally published	Yes
Event	38th International Cosmic Ray Conference, ICRC 2023 - Nagoya, Japan Duration: 26 Jul 2023 → 3 Aug 2023

Bibliographical note

Publisher Copyright:
© Copyright owned by the author(s) under the terms of the Creative Commons.

Cite this

@article{5e22b99cca774ada9ee6405670bcc266,

title = "Performance update of an 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. The traditional approach to data analysis in this field is to apply quality cuts, optimized using Monte Carlo simulations, on the data acquired to maximize sensitivity. Subsequent steps of the analysis typically use the surviving events to calculate one set of instrument response functions (IRFs) to physically interpret the results. However, an alternative approach is the use of event types, as implemented in experiments such as the Fermi-LAT. This approach divides events 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. In previous works we demonstrated that event types, classified using Machine Learning methods according to their expected angular reconstruction quality, have the potential to significantly improve the CTA angular and energy resolution of a point-like source analysis. Now, we validated the production of event-type wise full-enclosure IRFs, ready to be used with science tools (such as Gammapy and ctools). We will report on the impact of using such an event-type classification on CTA high-level performance, compared to the traditional procedure.",

author = "\{CTA consortium\} and J. Bernete and O. Gueta and T. Hassan and M. Linhoff and G. Maier and A. Sinha and K. Abe and S. Abe and A. Acharyya and R. Adam and A. Aguasca-Cabot and I. Agudo and J. Alfaro and N. Alvarez-Crespo and \{Alves Batista\}, R. and Amans, \{J. P.\} and E. Amato and F. Ambrosino and Ang{\"u}ner, \{E. O.\} and Antonelli, \{L. A.\} and C. Aramo and C. Arcaro and L. Arrabito and K. Asano and J. Aschersleben and H. Ashkar and \{Augusto Stuani\}, L. and D. Baack and M. Backes and C. Balazs and M. Balbo and \{Baquero Larriva\}, A. and \{Barbosa Martins\}, V. and \{de Almeida\}, \{U. Barres\} and Barrio, \{J. A.\} and D. Bastieri and Batista, \{P. I.\} and I. Batkovic and R. Batzofin and J. Baxter and G. Beck and \{Becker Tjus\}, J. and L. Beiske and D. Belardinelli and W. Benbow and E. Bernardini and \{Bernete Medrano\}, J. and K. Bernl{\"o}hr and A. Berti and A. Pe'er",

note = "Publisher Copyright: {\textcopyright} Copyright owned by the author(s) under the terms of the Creative Commons.; 38th International Cosmic Ray Conference, ICRC 2023 ; Conference date: 26-07-2023 Through 03-08-2023",

year = "2024",

month = sep,

day = "27",

language = "אנגלית",

volume = "444",

journal = "Proceedings of Science",

issn = "1824-8039",

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T1 - Performance update of an event-type based analysis for the Cherenkov Telescope Array

AU - CTA consortium

AU - Bernete, J.

AU - Gueta, O.

AU - Hassan, T.

AU - Linhoff, M.

AU - Maier, G.

AU - Sinha, A.

AU - Abe, K.

AU - Abe, S.

AU - Acharyya, A.

AU - Adam, R.

AU - Aguasca-Cabot, A.

AU - Agudo, I.

AU - Alfaro, J.

AU - Alvarez-Crespo, N.

AU - Alves Batista, R.

AU - Amans, J. P.

AU - Amato, E.

AU - Ambrosino, F.

AU - Angüner, E. O.

AU - Antonelli, L. A.

AU - Aramo, C.

AU - Arcaro, C.

AU - Arrabito, L.

AU - Asano, K.

AU - Aschersleben, J.

AU - Ashkar, H.

AU - Augusto Stuani, L.

AU - Baack, D.

AU - Backes, M.

AU - Balazs, C.

AU - Balbo, M.

AU - Baquero Larriva, A.

AU - Barbosa Martins, V.

AU - de Almeida, U. Barres

AU - Barrio, J. A.

AU - Bastieri, D.

AU - Batista, P. I.

AU - Batkovic, I.

AU - Batzofin, R.

AU - Baxter, J.

AU - Beck, G.

AU - Becker Tjus, J.

AU - Beiske, L.

AU - Belardinelli, D.

AU - Benbow, W.

AU - Bernardini, E.

AU - Bernete Medrano, J.

AU - Bernlöhr, K.

AU - Berti, A.

AU - Pe'er, A.

PY - 2024/9/27

Y1 - 2024/9/27

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. The traditional approach to data analysis in this field is to apply quality cuts, optimized using Monte Carlo simulations, on the data acquired to maximize sensitivity. Subsequent steps of the analysis typically use the surviving events to calculate one set of instrument response functions (IRFs) to physically interpret the results. However, an alternative approach is the use of event types, as implemented in experiments such as the Fermi-LAT. This approach divides events 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. In previous works we demonstrated that event types, classified using Machine Learning methods according to their expected angular reconstruction quality, have the potential to significantly improve the CTA angular and energy resolution of a point-like source analysis. Now, we validated the production of event-type wise full-enclosure IRFs, ready to be used with science tools (such as Gammapy and ctools). We will report on the impact of using such an event-type classification on CTA high-level performance, compared to the traditional 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. The traditional approach to data analysis in this field is to apply quality cuts, optimized using Monte Carlo simulations, on the data acquired to maximize sensitivity. Subsequent steps of the analysis typically use the surviving events to calculate one set of instrument response functions (IRFs) to physically interpret the results. However, an alternative approach is the use of event types, as implemented in experiments such as the Fermi-LAT. This approach divides events 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. In previous works we demonstrated that event types, classified using Machine Learning methods according to their expected angular reconstruction quality, have the potential to significantly improve the CTA angular and energy resolution of a point-like source analysis. Now, we validated the production of event-type wise full-enclosure IRFs, ready to be used with science tools (such as Gammapy and ctools). We will report on the impact of using such an event-type classification on CTA high-level performance, compared to the traditional procedure.

UR - https://www.scopus.com/pages/publications/85212295266

M3 - ???researchoutput.researchoutputtypes.contributiontojournal.conferencearticle???

AN - SCOPUS:85212295266

SN - 1824-8039

VL - 444

JO - Proceedings of Science

JF - Proceedings of Science

M1 - 738

T2 - 38th International Cosmic Ray Conference, ICRC 2023

Y2 - 26 July 2023 through 3 August 2023

ER -

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

Abstract

Bibliographical note

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