Studying deep convolutional neural networks with hexagonal lattices for imaging atmospheric Cherenkov Telescope event reconstruction

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F21%3A00552188" target="_blank" >RIV/68378271:_____/21:00552188 - isvavai.cz</a>
Result on the web
<a href="https://pos.sissa.it/358/753/pdf" target="_blank" >https://pos.sissa.it/358/753/pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.22323/1.358.0753" target="_blank" >10.22323/1.358.0753</a>

Result language
angličtina
Original language name
Studying deep convolutional neural networks with hexagonal lattices for imaging atmospheric Cherenkov Telescope event reconstruction
Original language description
Deep convolutional neural networks (DCNs) are a promising machine learning technique to reconstruct events recorded by imaging atmospheric Cherenkov telescopes (IACTs), but require optimization to reach full performance. One of the most pressing challenges is processing raw images captured by cameras made of hexagonal lattices of photo-multipliers, a common layout among IACT cameras which topologically differs from the square lattices conventionally expected, as their input data, by DCN models. Strategies directed to tackle this challenge range from the conversion of the hexagonal lattices onto square lattices by means of oversampling or interpolation to the implementation of hexagonal convolutional kernels. In this contribution we present a comparison of several of those strategies, using DCN models trained on simulated IACT data.n
Czech name
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Czech description
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Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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