The analysis of VERITAS muon images using convolutional neural networks

TL;DR

This paper explores using convolutional neural networks to classify muon images in IACT data, aiming to improve gamma-ray and cosmic-ray separation and aid in instrument calibration.

Contribution

It introduces a CNN-based approach for precise muon event classification and demonstrates potential for regression tasks like measuring muon radius and brightness.

Findings

CNN achieves promising classification accuracy.

CNN can be used for muon event regression.

Potential for improved IACT calibration.

Abstract

Imaging atmospheric Cherenkov telescopes (IACTs) are sensitive to rare gamma-ray photons, buried in the background of charged cosmic-ray (CR) particles, the flux of which is several orders of magnitude greater. The ability to separate gamma rays from CR particles is important, as it is directly related to the sensitivity of the instrument. This gamma-ray/CR-particle classification problem in IACT data analysis can be treated with the rapidly-advancing machine learning algorithms, which have the potential to outperform the traditional box-cut methods on image parameters. We present preliminary results of a precise classification of a small set of muon events using a convolutional neural networks model with the raw images as input features. We also show the possibility of using the convolutional neural networks model for regression problems, such as the radius and brightness measurement of muon events, which can be used to calibrate the throughput efficiency of IACTs.