Machine Learning Approach for Air Shower Recognition in EUSO-SPB Data

TL;DR

This paper develops a machine learning approach to identify and classify air shower events in EUSO-SPB1 data, improving detection efficiency and simplifying event analysis from space-based cosmic ray observations.

Contribution

It introduces a novel feature extraction and machine learning classification pipeline tailored for space-based air shower detection, combining supervised and unsupervised methods.

Findings

High classification accuracy on simulated data

Effective grouping of similar events with unsupervised learning

Reduced candidate event list for further analysis

Abstract

The main goal of The Extreme Universe Space Observatory on a Super Pressure Balloon (EUSO-SPB1) was to observe from above extensive air showers caused by ultra-high energy cosmic rays. EUSO-SPB1 uses a fluorescence detector that observes the atmosphere in a nadir observation mode from a near space altitude. During the 12-day flight, an onboard first level trigger detected more than \num{175000} candidate events. This paper presents an approach to recognize air showers in this dataset. The approach uses a feature extraction method to create a simpler representation of an event and then it uses established machine learning techniques to classify data into at least two classes - shower and noise. The machine learning models are trained on a set of air shower simulations put on top of the background observed during the flight and a set of events from the flight. We present the efficiency of the method on datasets of simulated events. The flight data events are also used in unsupervised learning methods to identify groups of events with similar features. The presented methods allow us to shorten the candidate events list and, thanks to the groups of similar events identified by the unsupervised methods, the classification of the triggered events is made simpler.