A Neural Resampler for Monte Carlo Reweighting with Preserved Uncertainties

S. Amoroso et al. (HSF Physics Event Generator WG), Challenges in Monte Carlo event generator software for High-Luminosity LHC, (2020), arXiv:2004.13687 [hep-ph]

Original

2004

Cited alongside, same era.

C. Badiali, F. Di Bello, G. Frattari, E. Gross, V. Ippolito, M. Kado, and J. Shlomi, Efficiency Parameterization with Neural Networks, (2020), arXiv:2004.02665 [hep-ex]

Original

2004

Cited alongside, same era.

J. R. Andersen, C. Gutschow, A. Maier, and S. Prestel, A positive resampler for monte carlo events with negative weights, (2020), arXiv:2005.09375 [hep-ph]

Original

2005

Cited alongside, same era.

E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol, and K. Kruger, Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed, (2020), arXiv:2005.05334 [physics.ins-det]

Original

2005

Cited alongside, same era.

M. Cacciari and G. P. Salam, Dispelling the N 3 N^{3} myth for the k t k_{t} jet-finder, Phys. Lett. B641

2006

Cited alongside, same era.

K. A. Nicoli, C. J. Anders, L. Funcke, T. Hartung, K. Jansen, P. Kessel, S. Nakajima, and P. Stornati, On Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models, (2020), arXiv:2007.07115 [hep-lat]

Original

2007

Cited alongside, same era.

A. van Hameren, OneLOop: For the evaluation of one-loop scalar functions, Comput. Phys. Commun. 182

Original

2011

Cited alongside, same era.

J. Bendavid, Efficient Monte Carlo Integration Using Boosted Decision Trees and Generative Deep Neural Networks, (2017), arXiv:1707.00028 [hep-ph]

Original

2017

Later among the works it cites.

F. Chollet, Keras, https://github.com/fchollet/keras (2017)

2017

Later among the works it cites.

M. Stoye, J. Brehmer, G. Louppe, J. Pavez, and K. Cranmer, Likelihood-free inference with an improved cross-entropy estimator, (2018), arXiv:1808.00973 [stat.ML]

Original

2018

Later among the works it cites.

S. Alonso-Monsalve and L. H. Whitehead, Image-based model parameter optimization using Model-Assisted Generative Adversarial Networks 10.1109/TNNLS.2020.2969327 (2018), arXiv:1812.00879 [cs.CV]

Original

2018

Later among the works it cites.

V. Chekalina, E. Orlova, F. Ratnikov, D. Ulyanov, A. Ustyuzhanin, and E. Zakharov, Generative Models for Fast Calorimeter Simulation.LHCb case (2018) arXiv:1812.01319 [physics.data-an]

Original

2018

Later among the works it cites.

Deep generative models for fast shower simulation in ATLAS, ATL-SOFT-PUB-2018-001 (2018)

2018

Later among the works it cites.

F. Carminati, A. Gheata, G. Khattak, P. Mendez Lorenzo, S. Sharan, and S. Vallecorsa, Three dimensional Generative Adversarial Networks for fast simulation, Proceedings, 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017): Seattle, WA, USA, August 21-25, 2017 1085

2018

Later among the works it cites.

S. Vallecorsa, Generative models for fast simulation, Proceedings, 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017): Seattle, WA, USA, August 21-25, 2017 1085

2018

Later among the works it cites.

K. Datta, D. Kar, and D. Roy, Unfolding with Generative Adversarial Networks, (2018), arXiv:1806.00433 [physics.data-an]

Original

2018

Later among the works it cites.

P. Musella and F. Pandolfi, Fast and Accurate Simulation of Particle Detectors Using Generative Adversarial Networks, Comput. Softw. Big Sci. 2

Original

2018

Later among the works it cites.

M. Erdmann, L. Geiger, J. Glombitza, and D. Schmidt, Generating and refining particle detector simulations using the Wasserstein distance in adversarial networks, Comput. Softw. Big Sci. 2

Original

2018

Later among the works it cites.

H. Erbin and S. Krippendorf, GANs for generating EFT models, (2018), arXiv:1809.02612 [cs.LG]

Original

2018

Later among the works it cites.

L. de Oliveira, M. Paganini, and B. Nachman, Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters, J. Phys. Conf. Ser. 1085

Original

2018

Later among the works it cites.

J. M. Urban and J. M. Pawlowski, Reducing Autocorrelation Times in Lattice Simulations with Generative Adversarial Networks, (2018), arXiv:1811.03533 [hep-lat]

Original

2018

Later among the works it cites.

M. D. Klimek and M. Perelstein, Neural Network-Based Approach to Phase Space Integration, (2018), arXiv:1810.11509 [hep-ph]

Original

2018

Later among the works it cites.

J. W. Monk, Deep Learning as a Parton Shower, (2018), arXiv:1807.03685 [hep-ph]

Original

2018

Later among the works it cites.

M. Tanabashi et al. (Particle Data Group), Review of particle physics, Phys. Rev. D 98

2018

Later among the works it cites.

J. Albrecht et al. (HEP Software Foundation), A Roadmap for HEP Software and Computing R&D for the 2020s, Comput. Softw. Big Sci. 3

Original

2019

Later among the works it cites.

S. Vallecorsa, F. Carminati, and G. Khattak, 3D convolutional GAN for fast simulation, Proceedings, 23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018): Sofia, Bulgaria, July 9-13, 2018 214

2019

Later among the works it cites.

K. Zhou, G. Endrodi, L.-G. Pang, and H. Stocker, Regressive and generative neural networks for scalar field theory, Phys. Rev. D100

Original

2019

Later among the works it cites.

K. Deja, T. Trzcinski, and u. Graczykowski, Generative models for fast cluster simulations in the TPC for the ALICE experiment, Proceedings, 23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018): Sofia, Bulgaria, July 9-13, 2018 214

2019

Later among the works it cites.

M. Erdmann, J. Glombitza, and T. Quast, Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network, Comput. Softw. Big Sci. 3

Original

2019

Later among the works it cites.

S. Farrell, W. Bhimji, T. Kurth, M. Mustafa, D. Bard, Z. Lukic, B. Nachman, and H. Patton, Next Generation Generative Neural Networks for HEP, EPJ Web Conf. 214

2019

Later among the works it cites.