Optimizing the Wasserstein GAN for TeV Gamma Ray Detection with VERITAS

TL;DR

This paper introduces an unsupervised Wasserstein GAN framework trained on VERITAS gamma-ray data to improve background rejection and gamma-ray detection in high-energy astrophysics.

Contribution

It proposes a novel WGAN-based method to analyze gamma-ray images, enhancing understanding and classification of real versus simulated events.

Findings

WGAN successfully models the latent space of gamma-ray images.

Preliminary results show improved background rejection capabilities.

Exploration of conditional parameters enhances model performance.

Abstract

The observation of very-high-energy (VHE, E>100 GeV) gamma rays is mediated by the imaging atmospheric Cherenkov technique (IACTs). At these energies, gamma rays interact with the atmosphere to create a cascade of electromagnetic air showers that are visible to the IACT cameras on the ground with distinct morphological and temporal features. However, hadrons with significantly higher incidence rates are also imaged with similar features, and must be distinguished with handpicked parameters extracted from the images. The advent of sophisticated deep learning models has enabled an alternative image analysis technique that has been shown to improve the detection of gamma rays, by improving background rejection. In this study, we propose an unsupervised Wasserstein Generative Adversarial Network (WGAN) framework trained on normalized, uncleaned stereoscopic shower images of real events from the VERITAS observatory to extract the landscape of their latent space and optimize against the corresponding inferred latent space of simulated gamma-ray events. We aim to develop a data driven approach to guide the understanding of the extracted features of real gamma-ray images, and will optimize the WGAN to calculate a probabilistic prediction of "gamma-ness" per event. In this poster, we present results of ongoing work toward the optimization of the WGAN, including the exploration of conditional parameters and multi-task learning.