Deep-learning-driven event reconstruction applied to simulated data from a single Large-Sized Telescope of CTA

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985815%3A_____%2F22%3A00567156" target="_blank" >RIV/67985815:_____/22:00567156 - isvavai.cz</a>
Alternative codes found
RIV/68378271:_____/22:00567156 RIV/61989592:15310/22:73617059
Result on the web
<a href="https://pos.sissa.it/395/771/pdf" target="_blank" >https://pos.sissa.it/395/771/pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.22323/1.395.0771" target="_blank" >10.22323/1.395.0771</a>

Result language
angličtina
Original language name
Deep-learning-driven event reconstruction applied to simulated data from a single Large-Sized Telescope of CTA
Original language description
We present the results obtained by applying deep learning techniques to the reconstruction of Monte Carlo simulated events from a single, next-generation IACT, the Large-Sized Telescope (LST) of the Cherenkov Telescope Array (CTA). We use CNNs to separate the gamma-ray-induced events from hadronic events and to reconstruct the properties of the former, comparing their performance to the standard reconstruction technique. Three independent implementations of CNN-based event reconstruction models have been utilized in this work, producing consistent results.
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
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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