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- W2786779607 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." @default.
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- W2786779607 date "2018-02-09" @default.
- W2786779607 modified "2023-09-27" @default.
- W2786779607 title "Generating and refining particle detector simulations using the Wasserstein distance in adversarial networks." @default.
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