Work

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

Year: 2019

Type: conference-paper

Abstract: 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.

Source: Journal of Physics Conference Series

DOI: 10.1088/1742-6596/1181/1/012048

Authors: E B Postnikov, A P Kryukov, S P Polyakov, D A Shipilov, D P Zhurov

Institutions: Lomonosov Moscow State University, Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Irkutsk State University

Language: English

Topic: Astrophysics and Cosmic Phenomena

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: diamond

Funder: Russian Science Foundation

Award: G2468721409