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SUMMARY:A machine learning method based on the extraction of essential fea
 tures for multimodal EAS data analysis.
DTSTART;VALUE=DATE-TIME:20261029T142000Z
DTEND;VALUE=DATE-TIME:20261029T143500Z
DTSTAMP;VALUE=DATE-TIME:20261025T080136Z
UID:indico-contribution-6216@cern.ch
DESCRIPTION:Speakers: Александр Крюков (НИИЯФ МГУ)\
 nTwo main types of ground-based experimental facilities are used to regist
 er EAS: atmospheric Cherenkov telescopes and arrays of Cherenkov detectors
 . The unique TAIGA scientific facility includes both types of installation
 s. The joint analysis of the data coming from these installations should i
 mprove the quality of the reconstructed EAS parameters compared to traditi
 onal methods that analyze the data separately. \n\nFor such a combined mul
 timodal analysis\, it is proposed to use a method based on extracting esse
 ntial features from experimental data using autoencoder type neural networ
 ks that do not require pre-marking the data for training. The main advanta
 ge of the proposed method over the traditional one is that data from vario
 us installations are naturally and easy aggregated at the level of essenti
 al features and further analyzed using classical deep neural networks to e
 xtract the physical parameters of the EAS.\n\nUsing the example of simulat
 ed Monte Carlo data for atmospheric Cherenkov telescopes and the Cherenkov
  detector array HiSCORE of the TAIGA installation\, it is shown that this 
 approach allows obtaining the parameters of the EAS in some cases signific
 antly better than traditional analysis methods.\n\nhttps://indico.particle
 .mephi.ru/event/626/contributions/6216/
LOCATION:Sevastopol Hotel
URL:https://indico.particle.mephi.ru/event/626/contributions/6216/
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