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Generating and Refining Particle Detector Simulations Using the Wasserstein Distance in Adversarial Networks


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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This work is supported by the Ministry of Innovation, Science and Research of the State of North Rhine-Westphalia, and the Federal Ministry of Education and Research (BMBF). We wish to thank Thorben Quast for his valuable comments on the manuscript.

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Correspondence to Martin Erdmann.

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On behalf of all authors, the corresponding author states that there is no conflict of interest.



See Tables 1, 2 and 3.

Table 1 Generator network as used in the WGAN to generate signal patterns
Table 2 Critic network as used in the WGAN to generate signal patterns
Table 3 Refiner network as used in the WGAN to refine signal traces. Residual shortcut connections are added after every pair of convolutions

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Erdmann, M., Geiger, L., Glombitza, J. et al. Generating and Refining Particle Detector Simulations Using the Wasserstein Distance in Adversarial Networks. Comput Softw Big Sci 2, 4 (2018). https://doi.org/10.1007/s41781-018-0008-x

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  • Deep learning
  • Adversarial networks
  • Wasserstein distance
  • Detector
  • Simulation