A Neural Network Approach for Selecting Track-like Events in Fluorescence Telescope Data
This work addresses data analysis challenges in astrophysics experiments, but it is incremental as it applies an existing neural network method to a new dataset.
The paper tackled the problem of detecting track-like events from fluorescence telescope data for ultra-high energy cosmic ray research, showing that a simple convolutional neural network can effectively identify these events.
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.