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S. Otten et al
Original
1901
Earlier work this paper cites.
B. Hashemi, N. Amin, K. Datta, D. Olivito, and M. Pierini, LHC analysis-specific datasets with Generative Adversarial Networks
Original
1901
Earlier work this paper cites.
R. Di Sipio, M. Faucci Giannelli, S. Ketabchi Haghighat, and S. Palazzo, DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC
Original
1903
Earlier work this paper cites.
J. Lin, W. Bhimji, and B. Nachman, Machine Learning Templates for QCD Factorization in the Search for Physics Beyond the Standard Model
Original
1903
Earlier work this paper cites.
S. Höche, S. Prestel, and H. Schulz, Simulation of Vector Boson Plus Many Jet Final States at the High Luminosity LHC
Original
1905
Earlier work this paper cites.
E. Bothmann et al
Original
1905
Earlier work this paper cites.
A. Butter, T. Plehn, and R. Winterhalder, How to GAN LHC Events
Original
1907
Earlier work this paper cites.
F. Bishara and M. Montull, (Machine) Learning Amplitudes for Faster Event Generation
Original
1912
Earlier work this paper cites.
A. Butter, T. Plehn, and R. Winterhalder, How to GAN Event Subtraction
Original
1912
Earlier work this paper cites.
D. Belayneh et al
Original
1912
Earlier work this paper cites.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, and R. Winterhalder, How to GAN away Detector Effects
Original
1912
Earlier work this paper cites.
G. P. Lepage, A new algorithm for adaptive multidimensional integration
1978
Earlier work this paper cites.
G. P. Lepage, VEGAS - an adaptive multi-dimensional integration program
1980
Earlier work this paper cites.
R. Kleiss, W. Stirling, and S. Ellis, A new monte carlo treatment of multiparticle phase space at high energies
1986
Earlier work this paper cites.
E. Bothmann, T. Janßen, M. Knobbe, T. Schmale, and S. Schumann, Exploring phase space with Neural Importance Sampling
Original
2001
Earlier work this paper cites.
C. Gao, J. Isaacson, and C. Krause, i-flow: High-dimensional Integration and Sampling with Normalizing Flows
Original
2001
Earlier work this paper cites.
C. Gao, S. Höche, J. Isaacson, C. Krause, and H. Schulz, Event Generation with Normalizing Flows
Original
2001
Earlier work this paper cites.

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Y. Alanazi, N. Sato, T. Liu, W. Melnitchouk, M. P. Kuchera, E. Pritchard, M. Robertson, R. Strauss, L. Velasco, and Y. Li, Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)
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2005
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Then

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D. J. Rezende and S. Mohamed, Variational inference with normalizing flows · 2015
Later among the works it cites.
A. Buckley, J. Ferrando, S. Lloyd, K. Nordström, B. Page, M. Rüfenacht, M. Schönherr, and G. Watt, Lhapdf6: parton density access in the LHC precision era
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L. de Oliveira, M. Paganini, and B. Nachman, Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
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Later among the works it cites.
A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, and A. A. Bharath, Generative adversarial networks: An overview
2018
Later among the works it cites.
M. Paganini, L. de Oliveira, and B. Nachman, Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters
Original
2018

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Reading the bibliography…

2020

How to GAN Event Unweighting

Backes, Mathias, Butter, Anja, Plehn, Tilman et al.

Understand

Event generation with neural networks has seen significant progress recently.

The big open question is still how such new methods will accelerate LHC simulations to the level required by upcoming LHC runs.
We target a known bottleneck of standard simulations and show how their unweighting procedure can be improved by generative networks.
This can, potentially, lead to a very significant gain in simulation speed.

Built on

S. Otten et al
Original
1901
Earlier work this paper cites.
B. Hashemi, N. Amin, K. Datta, D. Olivito, and M. Pierini, LHC analysis-specific datasets with Generative Adversarial Networks
Original
1901
Earlier work this paper cites.
R. Di Sipio, M. Faucci Giannelli, S. Ketabchi Haghighat, and S. Palazzo, DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC
Original
1903
Earlier work this paper cites.
J. Lin, W. Bhimji, and B. Nachman, Machine Learning Templates for QCD Factorization in the Search for Physics Beyond the Standard Model
Original
1903
Earlier work this paper cites.
S. Höche, S. Prestel, and H. Schulz, Simulation of Vector Boson Plus Many Jet Final States at the High Luminosity LHC
Original
1905
Earlier work this paper cites.
E. Bothmann et al
Original
1905
Earlier work this paper cites.
A. Butter, T. Plehn, and R. Winterhalder, How to GAN LHC Events
Original
1907
Earlier work this paper cites.
F. Bishara and M. Montull, (Machine) Learning Amplitudes for Faster Event Generation
Original
1912
Earlier work this paper cites.
A. Butter, T. Plehn, and R. Winterhalder, How to GAN Event Subtraction
Original
1912
Earlier work this paper cites.
D. Belayneh et al
Original
1912
Earlier work this paper cites.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, and R. Winterhalder, How to GAN away Detector Effects
Original
1912
Earlier work this paper cites.
G. P. Lepage, A new algorithm for adaptive multidimensional integration
1978
Earlier work this paper cites.
G. P. Lepage, VEGAS - an adaptive multi-dimensional integration program
1980
Earlier work this paper cites.
R. Kleiss, W. Stirling, and S. Ellis, A new monte carlo treatment of multiparticle phase space at high energies
1986
Earlier work this paper cites.
E. Bothmann, T. Janßen, M. Knobbe, T. Schmale, and S. Schumann, Exploring phase space with Neural Importance Sampling
Original
2001
Earlier work this paper cites.
C. Gao, J. Isaacson, and C. Krause, i-flow: High-dimensional Integration and Sampling with Normalizing Flows
Original
2001
Earlier work this paper cites.
C. Gao, S. Höche, J. Isaacson, C. Krause, and H. Schulz, Event Generation with Normalizing Flows
Original
2001
Earlier work this paper cites.

Similar

Y. Alanazi, N. Sato, T. Liu, W. Melnitchouk, M. P. Kuchera, E. Pritchard, M. Robertson, R. Strauss, L. Velasco, and Y. Li, Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)
Original
2001
Cited alongside, same era.
B. Nachman and D. Shih, Anomaly Detection with Density Estimation
Original
2001
Cited alongside, same era.
S. Badger and J. Bullock, Using neural networks for efficient evaluation of high multiplicity scattering amplitudes
Original
2002
Cited alongside, same era.
E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol, and K. Krüger, Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
Original
2005
Cited alongside, same era.

Then

JMLR.org, 2015
D. J. Rezende and S. Mohamed, Variational inference with normalizing flows · 2015
Later among the works it cites.
A. Buckley, J. Ferrando, S. Lloyd, K. Nordström, B. Page, M. Rüfenacht, M. Schönherr, and G. Watt, Lhapdf6: parton density access in the LHC precision era
2015
Later among the works it cites.
L. de Oliveira, M. Paganini, and B. Nachman, Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
Original
2017
Later among the works it cites.
A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, and A. A. Bharath, Generative adversarial networks: An overview
2018
Later among the works it cites.
M. Paganini, L. de Oliveira, and B. Nachman, Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters
Original
2018

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

O. Knapp, G. Dissertori, O. Cerri, T. Q. Nguyen, J.-R. Vlimant, and M. Pierini, Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark

Original

2005

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NNPDF, L. Del Debbio, S. Forte, J. I. Latorre, A. Piccione, and J. Rojo, Unbiased determination of the proton structure function F(2)**p with faithful uncertainty estimation

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M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, A. Rousselot, R. Winterhalder, L. Ardizzone, and U. Köthe, Invertible Networks or Partons to Detector and Back Again

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