PoS - Proceedings of Science
Volume 444 - 38th International Cosmic Ray Conference (ICRC2023) - Cosmic-Ray Physics (Indirect, CRI)
Machine Learning Techniques for the EUSO-SPB2 Fluorescence Telescope
G. Filippatos* and M. Zotov
Full text: pdf
Pre-published on: August 17, 2023
Published on:
The Extreme Universe Space Observatory on a Super Pressure Balloon 2 (EUSO-SPB2) is the
most advanced balloon mission undertaken by the JEM-EUSO collaboration. EUSO-SPB2 is
built on the experience of previous stratosphere missions, EUSO-Balloon and EUSO-SPB, and of
the Mini-EUSO space mission currently active onboard the International Space Station. EUSO-
SPB2 is equipped with two instruments: a fluorescence telescope aimed at registering ultra-high
energy cosmic rays (UHECRs) with an energy above 2 EeV and a Cherenkov telescope built to
measure direct Cherenkov emission from cosmic rays with energies above 1 PeV. The EUSO-SPB2
mission will provide pioneering observations on the path towards a space-based multi-messenger
observatory. As such, a special attention was paid to the development of triggers and other software
aimed at comprehensive data analysis. A whole number of methods based on machine learning
(ML) and neural networks was developed during the construction of the experiment and a few
others are under active development. Here we provide a brief review of the ML-based methods
already implemented in the instrument and the ground software and report preliminary results on
the ML-based reconstruction of UHECR parameters for the fluorescence telescope
DOI: https://doi.org/10.22323/1.444.0234
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