First IACT waveform analysis based on deep convolutional neural networks using CTLearn

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985815%3A_____%2F25%3A00646040" target="_blank" >RIV/67985815:_____/25:00646040 - isvavai.cz</a>
Alternative codes found
RIV/68378271:_____/25:00646040 RIV/61989592:15310/25:73635264
Result on the web
<a href="https://pos.sissa.it/501/758/pdf" target="_blank" >https://pos.sissa.it/501/758/pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.22323/1.501.0758" target="_blank" >10.22323/1.501.0758</a>

Result language
angličtina
Original language name
First IACT waveform analysis based on deep convolutional neural networks using CTLearn
Original language description
Imaging atmospheric Cherenkov telescopes (IACTs) detect extended air showers (EASs) generated when very-high-energy (VHE) gamma rays or cosmic rays interact with the Earth's atmosphere. Cherenkov photons produced during an EAS are captured by fast-imaging cameras, which record both the spatial and temporal development of the shower, along with calorimetric data. By analyzing these recordings, the properties of the original VHE particle—such as its type, energy, and direction of arrival—can be reconstructed through machine learning techniques. This contribution focuses on the Large-Sized Telescopes (LSTs) of the Cherenkov Telescope Array Observatory, a next-generation ground-based gamma-ray observatory. LSTs are responsible for reconstructing lower-energy gamma rays in the tens of GeV range. We explore a novel event reconstruction technique based on deep convolutional neural networks (CNNs) applied on calibrated and cleaned waveforms of the IACT camera pixels using CTLearn. Our approach explicitly incorporates the time development of the shower, enabling a more accurate reconstruction of the event. This method eliminates the need for charge integration or handcrafted feature extraction, allowing the model to directly learn from waveform data.
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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