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

• URL: http://arxiv.org/abs/2212.03787v2
• Date: Wed, 5 Apr 2023 10:46:12 GMT
• Title: A Neural Network Approach for Selecting Track-like Events in Fluorescence Telescope Data
• Authors: Mikhail Zotov, Denis Sokolinskii (for the JEM-EUSO collaboration)
• Abstract summary: 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. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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