Machine Learning Approach for Air Shower Recognition in EUSO-SPB Data
This work addresses data analysis for cosmic ray detection in astrophysics, but it is incremental as it applies established methods to a new dataset.
The paper tackles the problem of recognizing air showers from ultra-high energy cosmic rays in EUSO-SPB1 data, using machine learning to classify events and reduce a candidate list of over 175,000 events, with efficiency evaluated on simulated datasets.
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.