Reconstruction of sub-threshold events of cosmic-ray radio detectors using an autoencoder

TL;DR

This paper presents an autoencoder-based method to suppress background noise in radio detection data of cosmic-ray air showers, enabling detection of lower-energy events with improved resolution.

Contribution

The study introduces a novel autoencoder architecture tailored for background suppression in cosmic-ray radio data, enhancing detection sensitivity for sub-threshold events.

Findings

Successfully developed and trained an autoencoder for background suppression.

Improved detection threshold allows reconstruction of events below 0.1 EeV.

Enhanced angular and energy resolution for low-energy cosmic-ray events.

Abstract

Radio detection of air showers produced by ultra-high energy cosmic rays is a cost-effective technique for the next generation of sparse arrays. The performance of this technique strongly depends on the environmental background, which has different constituents, namely anthropogenic radio frequency interference, synchrotron galactic radiation and others. These components have recognizable features, which can help for background suppression. A powerful method for handling this is the application of convolution neural networks with a specific architecture called autoencoder. By suppressing unwanted signatures, the autoencoder keeps the signal-like ones. We have successfully developed and trained an autoencoder, which is now applied to the data from Tunka-Rex. We show the procedures of the training and optimization of the network including benchmarks of different architectures. Using the autoencoder, we improved the standard analysis of Tunka-Rex in order to lower the threshold of the detection. This enables the reconstructing of sub-threshold events with energies lower than 0.1 EeV with satisfactory angular and energy resolutions.