Generation of the Ground Detector Readings of the Telescope Array Experiment and the Search for Anomalies Using Neural Networks

Abstract

We report on the development of neural networks for generating readings from Telescope Array’s surface detectors with the largest registered integral signal. To achieve this goal, we implemented generative Wasserstein adversarial networks with the gradient penalty. The data used to train the model was generated using the Monte Carlo method. We obtained visually similar data which are consistent with the physics of the underlying processes. The anomaly search method can be employed to identify discrepancies between real and simulated data, as well as to introduce a quantitative measure of similarity between the real detector readings and those generated by the neural network’s readings.