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

Bernete, J. ; Carlile, C. LU ; Dravins, D. LU orcid and Zuriaga-Puig, J. (2024)
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.... (More)
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. © Copyright owned by the author(s) under the terms of the Creative Commons. (Less)
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author
; ; and
author collaboration
organization
publishing date
type
Contribution to conference
publication status
published
subject
keywords
Cerenkov counters, Space telescopes, Astroparticle physics, Cherenkov telescope arrays, Event Types, Gamma-rays, Instrument response functions, Performance, Reconstruction quality, Sub-samples, Type-based analysis, Very high energies, Cosmology
external identifiers
  • scopus:85212295266
DOI
10.22323/1.444.0738
language
English
LU publication?
yes
id
2b94f9d3-4cbb-4dbf-a112-4cc0c7f6fabe
date added to LUP
2025-12-01 09:34:30
date last changed
2025-12-01 09:35:47
@misc{2b94f9d3-4cbb-4dbf-a112-4cc0c7f6fabe,
  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. © Copyright owned by the author(s) under the terms of the Creative Commons.}},
  author       = {{Bernete, J. and Carlile, C. and Dravins, D. and Zuriaga-Puig, J.}},
  keywords     = {{Cerenkov counters; Space telescopes; Astroparticle physics; Cherenkov telescope arrays; Event Types; Gamma-rays; Instrument response functions; Performance; Reconstruction quality; Sub-samples; Type-based analysis; Very high energies; Cosmology}},
  language     = {{eng}},
  title        = {{Performance update of an event-type based analysis for the Cherenkov Telescope Array}},
  url          = {{http://dx.doi.org/10.22323/1.444.0738}},
  doi          = {{10.22323/1.444.0738}},
  year         = {{2024}},
}