A Deep Learning-based Reconstruction of Cosmic Ray-induced Air Showers

TL;DR

This paper presents a deep learning method for reconstructing cosmic ray-induced air showers using simulated detector data, achieving competitive accuracy in determining shower properties and directions.

Contribution

It introduces a novel deep learning approach converting detector responses into image-like data for improved air shower reconstruction.

Findings

Reconstruction accuracy is competitive with traditional methods.

Higher cosmic ray energies lead to better resolution.

Deep learning enhances the extraction of shower parameters.

Abstract

We describe a method of reconstructing air showers induced by cosmic rays using deep learning techniques. We simulate an observatory consisting of ground-based particle detectors with fixed locations on a regular grid. The detector's responses to traversing shower particles are signal amplitudes as a function of time, which provide information on transverse and longitudinal shower properties. In order to take advantage of convolutional network techniques specialized in local pattern recognition, we convert all information to the image-like grid of the detectors. In this way, multiple features, such as arrival times of the first particles and optimized characterizations of time traces, are processed by the network. The reconstruction quality of the cosmic ray arrival direction turns out to be competitive with an analytic reconstruction algorithm. The reconstructed shower direction, energy and shower depth show the expected improvement in resolution for higher cosmic ray energy.