Deep learning-based stereoscopic event reconstruction for CTAO using CTLearn

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985815%3A_____%2F25%3A00646019" target="_blank" >RIV/67985815:_____/25:00646019 - isvavai.cz</a>
Alternative codes found
RIV/68378271:_____/25:00646019
Result on the web
<a href="https://pos.sissa.it/501/757/pdf" target="_blank" >https://pos.sissa.it/501/757/pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.22323/1.501.0757" target="_blank" >10.22323/1.501.0757</a>

Result language
angličtina
Original language name
Deep learning-based stereoscopic event reconstruction for CTAO using CTLearn
Original language description
The Cherenkov Telescope Array Observatory (CTAO), a next-generation ground-based gamma-ray observatory, will be composed of two arrays of multiple imaging atmospheric Cherenkov telescopes (IACTs) located in both the Northern and Southern Hemispheres. Its goal is to enhance the sensitivity of current instruments by a factor of five to ten over an energy range from 20 GeV to over 300 TeV. IACT arrays are used to probe the very-high-energy (VHE) gamma-ray sky, operating by simultaneously observing air showers triggered by the interaction of VHE gamma rays and cosmic rays with the atmosphere. Cherenkov photons produced by these showers create a stereoscopic record of the event. By reconstructing the event using machine learning techniques, the properties of the originating VHE particle—including its type, energy, and incoming direction—can be determined. In this contribution, we present a fully deep-learning-driven approach to reconstruct simulated, stereoscopic IACT events using CTLearn. CTLearn is a package designed for loading and manipulating IACT data and for running deep learning models with pixel-wise camera data as input.
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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