Generating and Refining Particle Detector Simulations Using the Wasserstein Distance in Adversarial Networks
Erdmann M, Geiger L, Glombitza J, Schmidt D (2018)
Publication Language: English
Publication Type: Journal article
Publication year: 2018
Journal
Book Volume: 2
Article Number: 4
Journal Issue: 1
URI: https://link.springer.com/article/10.1007/s41781-018-0008-x
DOI: 10.1007/s41781-018-0008-x
Open Access Link: https://link.springer.com/article/10.1007/s41781-018-0008-x
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.
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Germany (DE)How to cite
APA:
Erdmann, M., Geiger, L., Glombitza, J., & Schmidt, D. (2018). Generating and Refining Particle Detector Simulations Using the Wasserstein Distance in Adversarial Networks. Computing and Software for Big Science, 2(1). https://doi.org/10.1007/s41781-018-0008-x
MLA:
Erdmann, Martin, et al. "Generating and Refining Particle Detector Simulations Using the Wasserstein Distance in Adversarial Networks." Computing and Software for Big Science 2.1 (2018).
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