Abstract
The Cherenkov Telescope Array (CTA), conceived as an array of tens of imaging atmospheric Cherenkov telescopes (IACTs), is an international project for a next-generation ground-based gamma-ray observatory, aiming to improve on the sensitivity of current-generation instruments a factor of five to ten and provide energy coverage from 20 GeV to more than 300 TeV. Arrays of IACTs probe the very-high-energy gamma-ray sky. Their working principle consists of the simultaneous observation of air showers initiated by the interaction of very-high-energy gamma rays and cosmic rays with the atmosphere. Cherenkov photons induced by a given shower are focused onto the camera plane of the telescopes in the array, producing a multi-stereoscopic record of the event. This image contains the longitudinal development of the air shower, together with its spatial, temporal, and calorimetric information. The properties of the originating very-high-energy particle (type, energy, and incoming direction) can be inferred from those images by reconstructing the full event using machine learning techniques. In this contribution, we present a purely deep-learning driven, full-event reconstruction of simulated, stereoscopic IACT events using CTLearn. CTLearn is a package that includes modules for loading and manipulating IACT data and for running deep learning models, using pixel-wise camera data as input.
| Original language | English (US) |
|---|---|
| Article number | 730 |
| Journal | Proceedings of Science |
| Volume | 395 |
| State | Published - Mar 18 2022 |
| Externally published | Yes |
| Event | 37th International Cosmic Ray Conference, ICRC 2021 - Virtual, Berlin, Germany Duration: Jul 12 2021 → Jul 23 2021 |
Bibliographical note
Funding Information:This work was conducted in the context of the CTA Analysis and Simulations Working Group. We gratefully acknowledge financial support from the agencies and organizations listed in this link. TM acknowledges support from PID2019-104114RB-C32. DN and JLC acknowledges partial support from The European Science Cluster of Astronomy & Particle Physics ESFRI Research Infrastructures funded by the European Union's Horizon 2020 research and innovation program under Grant Agreement no. 824064. This work was performed using resources provided by the Cambridge Service for Data Driven Discovery (CSD3) operated by the University of Cambridge Research Computing Service (www.csd3.cam.ac.uk), provided by Dell EMC and Intel using Tier-2 funding from the Engineering and Physical Sciences Research Council (capital grant EP/P020259/1), and DiRAC funding from the Science and Technology Facilities Council (STFC, www.dirac.ac.uk).This work used IRIS computing resources funded by the STFC. SS acknowledges an STFC PhD studentship. We acknowledge the support of NVIDIA Corporation with the donation of a Titan X Pascal GPU used for part of this research.
Funding Information:
This work was conducted in the context of the CTA Analysis and Simulations Working Group. We gratefully acknowledge financial support from the agencies and organizations listed in this link. TM acknowledges support from PID2019-104114RB-C32. DN and JLC acknowledges partial support from The European Science Cluster of Astronomy & Particle Physics ESFRI Research Infrastructures funded by the European Union’s Horizon 2020 research and innovation program under Grant Agreement no. 824064. This work was performed using resources provided by the Cambridge Service for Data Driven Discovery (CSD3) operated by the University of Cambridge Research Computing Service (www.csd3.cam.ac.uk), provided by Dell EMC and Intel using Tier-2 funding from the Engineering and Physical Sciences Research Council (capital grant EP/P020259/1), and DiRAC funding from the Science and Technology Facilities Council (STFC, www.dirac.ac.uk).This work used IRIS computing resources funded by the STFC. SS acknowledges an STFC PhD studentship. We acknowledge the support of NVIDIA Corporation with the donation of a Titan X Pascal GPU used for part of this research.
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