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Conditional Wasserstein Generative Adversarial Networks for Fast Detector Simulation
2021
EPJ Web of Conferences
Detector simulation in high energy physics experiments is a key yet computationally expensive step in the event simulation process. There has been much recent interest in using deep generative models as a faster alternative to the full Monte Carlo simulation process in situations in which the utmost accuracy is not necessary. In this work we investigate the use of conditional Wasserstein Generative Adversarial Networks to simulate both hadronization and the detector response to jets. Our model
doi:10.1051/epjconf/202125103055
fatcat:m3bftt4yt5g6zet2bgpyy4vcqi