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Event generators in high-energy nuclear and particle physics play an important role in facilitating studies of particle reactions.
Universal Approximation Using Feedforward Neural Networks: A Survey of Some Existing Methods, and Some New Results
F. Scarselli and A. C. Tsoi · 1998
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The CLAS Cherenkov detector
G. Adams et al · 2001
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Geant4-a simulation toolkit
S. Agostinelli et al · 2003
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Alpgen, a generator for hard multiparton processes in hadronic collisions
M. L. Mangano, F. Piccinini, A. D. Polosa, M. Moretti, and R. Pittau · 2003
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Tools for the simulation of hard hadronic collisions
M. L. Mangano and T. J. Stelzer · 2005
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Herwig++ Physics and Manual
M. Bahr et al · 2008
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A Brief Introduction to PYTHIA 8.1
T. Sjostrand, S. Mrenna, and P. Z. Skands · 2008
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Running Nuwro
J. Cezary · 2009
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Event generation with SHERPA 1.1
T. Gleisberg, S. Hoeche, F. Krauss, M. Schonherr, S. Schumann, F. Siegert, and J. Winter · 2009
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Delphes, a framework for fast simulation of a generic collider experiment
S. Ovyn, X. Rouby, and V. Lemaître · 2009
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The GENIE Neutrino Monte Carlo Generator
C. Andreopoulos et al · 2010
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Computing methods in high energy physics
S. Lehti and V. Karimaki · 2010
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Madgraph 5: going beyond
J. Alwall, M. Herquet, F. Maltoni, O. Mattelaer, and T. Stelzer · 2011
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Whizard—simulating multi-particle processes at lhc and ilc
W. Kilian, T. Ohl, and J. Reuter · 2011
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Dilepton production in proton-induced reactions at SIS energies with the GiBUU transport model
J. Weil, H. van Hees, and U. Mosel · 2012
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The monte carlo event generator acermc versions 2.0 to 3.8 with interfaces to pythia 6.4, herwig 6.5 and ariadne 4.1
B. P. Kersevan and E. Richter-Was · 2013
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The FLUKA Code: Developments and Challenges for High Energy and Medical Applications
T. T. Böhlen, F. Cerutti, M. P. W. Chin, A. Fassò, A. Ferrari, P. G. Ortega, A. Mairani, P. R. Sala, G. Smirnov, and V. Vlachoudis · 2014
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Generative adversarial networks
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Auto-encoding variational bayes
D. Kingma and M. Welling · 2014
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Conditional generative adversarial nets
M. Mirza and S. Osindero · 2014
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Hepsim: A repository with predictions for high-energy physics experiments
S. V. Chekanov · 2015
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On the decreasing power of kernel and distance based nonparametric hypothesis tests in high dimensions
A. Ramdas, S. J. Reddi, B. Póczos, A. Singh, and L. Wasserman · 2015
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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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Topics in optimal transportation
C. Villani · 2016
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Wasserstein gan
M. Arjovsky, S. Chintala, and L. Bottou · 2017
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DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC
R. Di Sipio, M. Faucci Giannelli, S. Ketabchi Haghighat, and S. Palazzo · 2019
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Lhc analysis-specific datasets with gans networks
B. Hashemi, N. Amin, K. Datta, D. Olivito, and M. Pierini · 2019
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hicGAN infers super resolution Hi-C data with generative adversarial networks
Q. Liu, H. Lv, and R. Jiang · 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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AI-based Monte Carlo event generator for electron-proton scattering
Y. Alanazi, P. Ambrozewicz, M. P. Kuchera, Y. Li, T. Liu, R. E. McClellan, W. Melnitchouk, E. Pritchard, M. Robertson, N. Sato, R. Strauss, and L. Velasco · 2020
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Variational inference: A review for statisticians
D. Blei, A. Kucukelbir, and J. McAuliffe · 2017
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Improved training of wasserstein gans
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville · 2017
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi · 2017
Cited alongside, same era.
Mmd gan: Towards deeper understanding of moment matching network
C. Li, W. Chang, Y. Cheng, Y. Yang, and B. Póczos · 2017
Cited alongside, same era.
Least squares generative adversarial networks
X. Mao, Q. Li, H. Xie, R. Y. K. Lau, Z. Wang, and S. P. Smolley · 2017
Cited alongside, same era.
Generating and refining particle detector simulations using the wasserstein distance in adversarial networks
M. Erdmann, L. Geiger, J. Glombitza, and D. Schmidt · 2018
Cited alongside, same era.
Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)
Y. Alanazi, N. Sato, T. Liu, W. Melnitchouk, M. P. Kuchera, E. Pritchard, M. Robertson, R. Strauss, L. Velasco, and Y. Li · 2020
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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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Computational challenges for MC event generation
A. Buckley · 2020
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Generative Networks for LHC events
A. Butter and T. Plehn · 2020
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Ganplifying event samples
A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman, and T. Plehn · 2020
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Event Generation with Normalizing Flows
C. Gao, S. Höche, J. Isaacson, C. Krause, and H. Schulz · 2020
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Normalizing flows: An introduction and review of current methods
I. Kobyzev, S. Prince, and M. Brubaker · 2020
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Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description
J. A. Martínez, T. Q. Nguyen, M. Pierini, M. Spiropulu, and V. Jean-Roch · 2020
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Uncertainties associated with gan-generated datasets in high energy physics
K. T. Matchev and P. Shyamsundar · 2020
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cFAT-GAN: Conditional simulation of electron-proton scattering events with variate beam energies by a Feature Augmented and Transformed Generative Adversarial Network
L. Velasco, Y. Alanazi, E. McClellan, P. Ambrozewicz, N. Sato, T. Liu, W. Melnitchouk, M. P. Kuchera, and Y. Li · 2020
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A data-driven event generator for hadron colliders using wasserstein generative adversarial network
S. Choi and J. Lim · 2021
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Foundations of a fast, data-driven, machine-learned simulator
J. N. Howard, S. Mandt, D. Whiteson, and Y. Yang · 2021
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Ai-assisted superresolution cosmological simulations
Y. Li, Y. Ni, R. A. C. Croft, T. Di Matteo, S. Bird, and Y. Feng · 2021
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