Advanced analysis methods for Imaging Atmospheric Cherenkov Telescope data with SSTCAM and VERITAS

Imaging Atmospheric Cherenkov Telescope arrays allow us to probe the gamma-ray sky from tens of GeV up to hundreds of TeV. They operate by stereoscopically imaging the Cherenkov light generated when an astrophysical gamma-ray interacts with Earth's atmosphere. In order to reject charged cosmic...

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Bibliographic Details
Main Author: Spencer, S
Other Authors: Cotter, G
Format: Thesis
Language:English
Published: 2021
Subjects:
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author Spencer, S
author2 Cotter, G
author_facet Cotter, G
Spencer, S
author_sort Spencer, S
collection OXFORD
description Imaging Atmospheric Cherenkov Telescope arrays allow us to probe the gamma-ray sky from tens of GeV up to hundreds of TeV. They operate by stereoscopically imaging the Cherenkov light generated when an astrophysical gamma-ray interacts with Earth's atmosphere. In order to reject charged cosmic ray events, and to reconstruct the direction and energy of the incident gamma-ray, machine learning methods are used in combination with parametric descriptions of the detected images. One potential means of improving performance for the next-generation Cherenkov Telescope Array (CTA) is to apply new deep learning methods in place of these parametric techniques. In this thesis, we explore the complexity of deploying deep learning methods, first considering the application of high precision timing data, and then testing such methods' performance on real observations from the current generation VERITAS array. Finally, we explore improvements to the modelling of Night Sky Background observed by Cherenkov instruments, that can be used to both inform the design of the Small Sized Telescope Camera (SSTCAM) for CTA, as well as potentially augment simulated data for deep-learning-based event classification.
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spelling oxford-uuid:dd29d691-98d4-45da-8565-fe00251110802022-06-09T12:13:22ZAdvanced analysis methods for Imaging Atmospheric Cherenkov Telescope data with SSTCAM and VERITASThesishttp://purl.org/coar/resource_type/c_db06uuid:dd29d691-98d4-45da-8565-fe0025111080AstrophysicsAstroparticle PhysicsEnglishHyrax Deposit2021Spencer, SCotter, GImaging Atmospheric Cherenkov Telescope arrays allow us to probe the gamma-ray sky from tens of GeV up to hundreds of TeV. They operate by stereoscopically imaging the Cherenkov light generated when an astrophysical gamma-ray interacts with Earth's atmosphere. In order to reject charged cosmic ray events, and to reconstruct the direction and energy of the incident gamma-ray, machine learning methods are used in combination with parametric descriptions of the detected images. One potential means of improving performance for the next-generation Cherenkov Telescope Array (CTA) is to apply new deep learning methods in place of these parametric techniques. In this thesis, we explore the complexity of deploying deep learning methods, first considering the application of high precision timing data, and then testing such methods' performance on real observations from the current generation VERITAS array. Finally, we explore improvements to the modelling of Night Sky Background observed by Cherenkov instruments, that can be used to both inform the design of the Small Sized Telescope Camera (SSTCAM) for CTA, as well as potentially augment simulated data for deep-learning-based event classification.
spellingShingle Astrophysics
Astroparticle Physics
Spencer, S
Advanced analysis methods for Imaging Atmospheric Cherenkov Telescope data with SSTCAM and VERITAS
title Advanced analysis methods for Imaging Atmospheric Cherenkov Telescope data with SSTCAM and VERITAS
title_full Advanced analysis methods for Imaging Atmospheric Cherenkov Telescope data with SSTCAM and VERITAS
title_fullStr Advanced analysis methods for Imaging Atmospheric Cherenkov Telescope data with SSTCAM and VERITAS
title_full_unstemmed Advanced analysis methods for Imaging Atmospheric Cherenkov Telescope data with SSTCAM and VERITAS
title_short Advanced analysis methods for Imaging Atmospheric Cherenkov Telescope data with SSTCAM and VERITAS
title_sort advanced analysis methods for imaging atmospheric cherenkov telescope data with sstcam and veritas
topic Astrophysics
Astroparticle Physics
work_keys_str_mv AT spencers advancedanalysismethodsforimagingatmosphericcherenkovtelescopedatawithsstcamandveritas