Prospects for the use of photosensor timing information with machine learning techniques in background rejection

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F21%3A00551835" target="_blank" >RIV/68378271:_____/21:00551835 - isvavai.cz</a>
Result on the web
<a href="https://pos.sissa.it/358/798/pdf" target="_blank" >https://pos.sissa.it/358/798/pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.22323/1.358.0798" target="_blank" >10.22323/1.358.0798</a>

Result language
angličtina
Original language name
Prospects for the use of photosensor timing information with machine learning techniques in background rejection
Original language description
Recent developments in machine learning (ML) techniques present a promising new analysis method for high-speed imaging in astroparticle physics experiments, for example with imaging atmospheric Cherenkov telescopes (IACTs). In particular, the use of timing information with new machine learning techniques provides a novel method for event classification. Previous work in this field has utilised images of the integrated charge from IACT camera photomultipliers, but the majority of current and upcoming IACT cameras have the capacity to read out the entire photosensor waveform following a trigger. As the arrival times of Cherenkov photons from extensive air showers (EAS) at the camera plane are dependent upon the altitude of their emission, these waveforms contain information useful for IACT event classification.
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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