12-23 July 2021
Online
Europe/Berlin timezone

The use of convolutional neural networks for processing images from multiple IACTs in the TAIGA experiment

13 Jul 2021, 12:00
1h 30m
04

04

Poster GAI | Gamma Ray Indirect Discussion

Speaker

Stanislav Polyakov (SINP MSU)

Description

TAIGA experiment uses hybrid detection system for cosmic and gamma rays that currently includes three imaging atmospheric Cherenkov telescopes (IACTs). Previously we used convolutional neural networks to select gamma ray events and estimate the energy of the gamma rays based on an image from a single telescope. Subsequently we adapted these techniques to use data from multiple telescopes, increasing the quality of selection and the accuracy of estimates. All the results have been obtained with the simulated data of TAIGA Monte Carlo software.

Keywords

deep learning; convolutional neural networks; gamma astronomy; extensive air shower; TAIGA; stereoscopic mode

Subcategory Experimental Methods & Instrumentation

Primary authors

Stanislav Polyakov (SINP MSU) Dr Alexander Kryukov (Lomonosov Moscow State University) Evgeny Postnikov (SINP MSU)

Presentation Materials

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