Gamma/Hadron Separation in Imaging Air Cherenkov Telescopes Using Deep Learning Libraries TensorFlow and PyTorch

In this work we compare two open source machine learning libraries, PyTorch and TensorFlow, as software platforms for rejecting hadron background events detected by imaging air Cherenkov telescopes (IACTs). Monte Carlo simulation for the TAIGA-IACT telescope is used to estimate background rejection...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1181; no. 1; pp. 12048 - 12053
Main Authors Postnikov, E B, Kryukov, A P, Polyakov, S P, Shipilov, D A, Zhurov, D P
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.02.2019
Subjects
Algorithms
Deep learning
Imaging
Libraries
Machine learning
Monte Carlo simulation
Neural networks
Telescopes
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Summary:In this work we compare two open source machine learning libraries, PyTorch and TensorFlow, as software platforms for rejecting hadron background events detected by imaging air Cherenkov telescopes (IACTs). Monte Carlo simulation for the TAIGA-IACT telescope is used to estimate background rejection quality. A wide variety of neural network algorithms provided by both libraries can easily be tested on various types of data, which is useful for various imaging air Cherenkov experiments. The work is a component of the Astroparticle.online project, which collaborates with the TAIGA and KASCADE experiments and welcomes any astroparticle experiment to join.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1181/1/012048