  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.
