Generating and refining particle detector simulations using the Wasserstein distance in adversarial networks

TL;DR

This paper introduces a novel adversarial network approach utilizing the Wasserstein distance to generate and refine particle detector simulations, improving the accuracy of cosmic ray event analysis.

Contribution

It presents new methods for generating variable detector patterns and refining simulated signals, enhancing the realism and utility of particle detector simulations.

Findings

Refined simulated signals better match real data distributions.

Training with refined data improves energy reconstruction accuracy.

The approach demonstrates effective application to cosmic ray detection.

Abstract

We use adversarial network architectures together with the Wasserstein distance to generate or refine simulated detector data. The data reflect two-dimensional projections of spatially distributed signal patterns with a broad spectrum of applications. As an example, we use an observatory to detect cosmic ray-induced air showers with a ground-based array of particle detectors. First we investigate a method of generating detector patterns with variable signal strengths while constraining the primary particle energy. We then present a technique to refine simulated time traces of detectors to match corresponding data distributions. With this method we demonstrate that training a deep network with refined data-like signal traces leads to a more precise energy reconstruction of data events compared to training with the originally simulated traces.