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We apply generative adversarial network (GAN) technology to build an event generator that simulates particle production in electron-proton scattering that is free of theoretical assumptions about underlying particle dynamics.
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Generative adversarial networks
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Monte Carlo Methods and Their Applications in Big Data Analysis
H. Ji and Y. Li · 2016
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C-rnn-gan: Continuous recurrent neural networks with adversarial training
O. Mogren · 2016
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Improved techniques for training gans
T. Salimans, I. J. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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Towards principled methods for training generative adversarial networks
M. Arjovsky and L. Bottou · 2017
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Do gans actually learn the distribution? an empirical study
S. Arora and Y. Zhang · 2017
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Efficient monte carlo integration using boosted decision trees and generative deep neural networks
J. Bendavid · 2017
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Learning particle physics by example: Location-aware generative adversarial networks for physics synthesis
L. de Oliveira, M. Paganini, and B. Nachman · 2017
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Improved training of wasserstein gans
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville · 2017
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Mmd gan: Towards deeper understanding of moment matching network
C. Li, W. Chang, Y. Cheng, Y. Yang, and B. Póczos · 2017
Accelerating science with generative adversarial networks: An application to 3d particle showers in multilayer calorimeters
M. Paganini, L. de Oliveira, and B. Nachman · 2018
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Calogan Simulating 3d high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks
M. Paganini, L. de Oliveira, and B. Nachman · 2018
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How to gan lhc events
A. Butter, T. Plehn, and R. Winterhalder · 2019
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Adversarial video generation on complex datasets
A. Clark, J. Donahue, and K. Simonyan · 2019
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Precise simulation of electromagnetic calorimeter showers using a wasserstein generative adversarial network
M. Erdmann, J. Glombitza, and T. Quast · 2019
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Lhc analysis-specific datasets with generative adversarial networks
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Wasserstein gan
S. Chintala M. Arjovsky and L. Bottou · 2017
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Unfolding with generative adversarial networks
K. Datta, D. Kar, and D. Roy · 2018
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A deep learning-based reconstruction of cosmic ray-induced air showers
M. Erdmann, J. Glombitza, and D. Walz · 2018
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A style-based generator architecture for generative adversarial networks
T. Karras, S. Laine, and T. Aila · 2018
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Fast and accurate simulation of particle detectors using generative adversarial networks
P. Musella and F. Pandolfi · 2018
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B. Hashemi, N. Amin, K. Datta, D. Olivito, and M. Pierini · 2019
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Event generation and statistical sampling for physics with deep generative models and a density information buffer
S. Otten, S. Caron, W. de Swart, M. van Beekveld, L. Hendriks, C. van Leeuwen, D. Podareanu, R. R. de Austri, and R. Verheyen · 2019
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How to gan away detector effects
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, and R. Winterhalder · 2020
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Ganplifying event samples
A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman, and T. Plehn · 2020
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DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC
R. Di Sipio, G. M. Faucci, H. S. Ketabchi, and S. Palazzo · 2020
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