A Neural Network Approach for Selecting Track-like Events in Fluorescence Telescope Data

Mikhail Zotov; Denis Sokolinskii

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arXiv · 2212.03787

A Neural Network Approach for Selecting Track-like Events in Fluorescence Telescope Data

Mikhail Zotov·Denis Sokolinskii

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Abstract

In 2016-2017, TUS, the world's first experiment for testing the possibility of registering ultra-high energy cosmic rays (UHECRs) by their fluorescent radiation in the night atmosphere of Earth was carried out. Since 2019, the Russian-Italian fluorescence telescope (FT) Mini-EUSO ("UV Atmosphere") has been operating on the ISS. The stratospheric experiment EUSO-SPB2, which will employ an FT for registering UHECRs, is planned for 2023. We show how a simple convolutional neural network can be effectively used to find track-like events in the variety of data obtained with such instruments.

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Mikhail Zotov, Denis Sokolinskii. 2022-12-07. A Neural Network Approach for Selecting Track-like Events in Fluorescence Telescope Data. https://arxiv.org/abs/2212.03787

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