Machine Learning for the EUSO-SPB2 Fluorescence Telescope Data Analysis

TL;DR

This paper discusses the development and application of machine learning methods for analyzing data from the EUSO-SPB2 fluorescence telescope, aiming to improve the detection and reconstruction of ultra-high energy cosmic rays.

Contribution

It introduces ML-based methods implemented in EUSO-SPB2 for data analysis and reports preliminary results on cosmic ray parameter reconstruction.

Findings

ML methods successfully implemented in the instrument and ground software

Preliminary results show promising reconstruction accuracy for UHECR parameters

Development of triggers and software enhances data analysis capabilities

Abstract

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.