Built on

S. Otten, S. Caron, W. de Swart, M. van Beekveld, L. Hendriks, C. van Leeuwen et al., Event Generation and Statistical Sampling for Physics with Deep Generative Models and a Density Information Buffer , 1901.00875
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 , 1901.05282
Original
1901
Earlier work this paper cites.
P. Azzi et al., Report from Working Group 1: Standard Model Physics at the HL-LHC and HE-LHC , CERN Yellow Rep. Monogr. 7
Original
1902
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 , JHEP 08
Original
1903
Earlier work this paper cites.
A. Butter, T. Plehn and R. Winterhalder, How to GAN LHC Events , SciPost Phys. 7
Original
1907
Earlier work this paper cites.
S. Carrazza and F.A. Dreyer, Lund jet images from generative and cycle-consistent adversarial networks , Eur. Phys. J. C 79
Original
1909
Earlier work this paper cites.
F. Bishara and M. Montull, (Machine) Learning amplitudes for faster event generation , 1912.11055
Original
1912
Earlier work this paper cites.
R. Kleiss, W. Stirling and S. Ellis, A New Monte Carlo Treatment of Multiparticle Phase Space at High-energies , Comput. Phys. Commun. 40
1986
Earlier work this paper cites.
Z. Bern, L.J. Dixon, D.C. Dunbar and D.A. Kosower, One loop n point gauge theory amplitudes, unitarity and collinear limits , Nucl. Phys. B 425
1994
Earlier work this paper cites.
Z. Bern and G. Chalmers, Factorization in one loop gauge theory , Nucl. Phys. B 447
1995
Earlier work this paper cites.
S. Catani and M. Seymour, A General algorithm for calculating jet cross-sections in NLO QCD , Nucl. Phys. B 485
1997
Earlier work this paper cites.
D.A. Kosower, Antenna factorization of gauge theory amplitudes , Phys. Rev. D 57
1998
Earlier work this paper cites.
Z. Bern, V. Del Duca, W.B. Kilgore and C.R. Schmidt, The infrared behavior of one loop QCD amplitudes at next-to-next-to leading order , Phys. Rev. D 60
1999
Earlier work this paper cites.
D.A. Kosower, All order collinear behavior in gauge theories , Nucl. Phys. B 552
1999
Earlier work this paper cites.
E. Bothmann, T. Janßen, M. Knobbe, T. Schmale and S. Schumann, Exploring phase space with Neural Importance Sampling , SciPost Phys. 8
Original
2001
Earlier work this paper cites.
C. Gao, S. Höche, J. Isaacson, C. Krause and H. Schulz, Event Generation with Normalizing Flows , Phys. Rev. D 101
Original
2001
Earlier work this paper cites.

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Y. Alanazi et al., Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN) , 2001.11103
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2001
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2002
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D.A. Kosower, Multiple singular emission in gauge theories , Phys. Rev. D 67
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Then

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro et al., TensorFlow: Large-scale machine learning on heterogeneous systems , 2015
2015
Later among the works it cites.
P. Baldi, K. Cranmer, T. Faucett, P. Sadowski and D. Whiteson, Parameterized neural networks for high-energy physics , Eur. Phys. J. C 76
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2016
Later among the works it cites.
S. Elfwing, E. Uchibe and K. Doya, Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning , arXiv e-prints (2017) arXiv:1702.03118 [ 1702.03118 ]
Original
2017
Later among the works it cites.
P. Ramachandran, B. Zoph and Q.V. Le, Searching for Activation Functions , arXiv e-prints (2017) arXiv:1710.05941 [ 1710.05941 ]
Original
2017
Later among the works it cites.
D.P. Kingma and J. Ba, Adam: A method for stochastic optimization , 2017

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2023

One-loop matrix element emulation with factorisation awareness

Maître, D., Truong, H.

Understand

In this article we present an emulation strategy for one-loop matrix elements.

This strategy is based on the factorisation properties of matrix elements and is an extension of the work presented in arXiv:2107.06625.
We show that a percent-level accuracy can be achieved even for large multiplicity processes.
The point accuracy obtained is such that it dwarfs the statistical accuracy of the training sample which allows us to use our model to augment the size of the training set by orders of magnitude without additional evaluations of expensive one-loop matrix elements.

Built on

S. Otten, S. Caron, W. de Swart, M. van Beekveld, L. Hendriks, C. van Leeuwen et al., Event Generation and Statistical Sampling for Physics with Deep Generative Models and a Density Information Buffer , 1901.00875
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 , 1901.05282
Original
1901
Earlier work this paper cites.
P. Azzi et al., Report from Working Group 1: Standard Model Physics at the HL-LHC and HE-LHC , CERN Yellow Rep. Monogr. 7
Original
1902
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 , JHEP 08
Original
1903
Earlier work this paper cites.
A. Butter, T. Plehn and R. Winterhalder, How to GAN LHC Events , SciPost Phys. 7
Original
1907
Earlier work this paper cites.
S. Carrazza and F.A. Dreyer, Lund jet images from generative and cycle-consistent adversarial networks , Eur. Phys. J. C 79
Original
1909
Earlier work this paper cites.
F. Bishara and M. Montull, (Machine) Learning amplitudes for faster event generation , 1912.11055
Original
1912
Earlier work this paper cites.
R. Kleiss, W. Stirling and S. Ellis, A New Monte Carlo Treatment of Multiparticle Phase Space at High-energies , Comput. Phys. Commun. 40
1986
Earlier work this paper cites.
Z. Bern, L.J. Dixon, D.C. Dunbar and D.A. Kosower, One loop n point gauge theory amplitudes, unitarity and collinear limits , Nucl. Phys. B 425
1994
Earlier work this paper cites.
Z. Bern and G. Chalmers, Factorization in one loop gauge theory , Nucl. Phys. B 447
1995
Earlier work this paper cites.
S. Catani and M. Seymour, A General algorithm for calculating jet cross-sections in NLO QCD , Nucl. Phys. B 485
1997
Earlier work this paper cites.
D.A. Kosower, Antenna factorization of gauge theory amplitudes , Phys. Rev. D 57
1998
Earlier work this paper cites.
Z. Bern, V. Del Duca, W.B. Kilgore and C.R. Schmidt, The infrared behavior of one loop QCD amplitudes at next-to-next-to leading order , Phys. Rev. D 60
1999
Earlier work this paper cites.
D.A. Kosower, All order collinear behavior in gauge theories , Nucl. Phys. B 552
1999
Earlier work this paper cites.
E. Bothmann, T. Janßen, M. Knobbe, T. Schmale and S. Schumann, Exploring phase space with Neural Importance Sampling , SciPost Phys. 8
Original
2001
Earlier work this paper cites.
C. Gao, S. Höche, J. Isaacson, C. Krause and H. Schulz, Event Generation with Normalizing Flows , Phys. Rev. D 101
Original
2001
Earlier work this paper cites.

Similar

Y. Alanazi et al., Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN) , 2001.11103
Original
2001
Cited alongside, same era.
S. Badger and J. Bullock, Using neural networks for efficient evaluation of high multiplicity scattering amplitudes , JHEP 06
Original
2002
Cited alongside, same era.
D.A. Kosower, Multiple singular emission in gauge theories , Phys. Rev. D 67
2003
Cited alongside, same era.
A. Gehrmann-De Ridder, T. Gehrmann and E.W.N. Glover, Antenna subtraction at NNLO , JHEP 09
2005
Cited alongside, same era.
B. Nachman and J. Thaler, Neural resampler for Monte Carlo reweighting with preserved uncertainties , Phys. Rev. D 102
Original
2007
Cited alongside, same era.

Then

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro et al., TensorFlow: Large-scale machine learning on heterogeneous systems , 2015
2015
Later among the works it cites.
P. Baldi, K. Cranmer, T. Faucett, P. Sadowski and D. Whiteson, Parameterized neural networks for high-energy physics , Eur. Phys. J. C 76
Original
2016
Later among the works it cites.
S. Elfwing, E. Uchibe and K. Doya, Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning , arXiv e-prints (2017) arXiv:1702.03118 [ 1702.03118 ]
Original
2017
Later among the works it cites.
P. Ramachandran, B. Zoph and Q.V. Le, Searching for Activation Functions , arXiv e-prints (2017) arXiv:1710.05941 [ 1710.05941 ]
Original
2017
Later among the works it cites.
D.P. Kingma and J. Ba, Adam: A method for stochastic optimization , 2017

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman and T. Plehn, GANplifying Event Samples , 2008.06545

Original

2008

Cited alongside, same era.

K. Dohi, Variational Autoencoders for Jet Simulation , 2009.04842

Original

2009

Cited alongside, same era.

X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks , in In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS’10). Society for Artificial Intelligence and Statistics , 2010

2010

Cited alongside, same era.

B. Stienen and R. Verheyen, Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows , SciPost Phys. 10

Original

2011

Cited alongside, same era.

V. Hirschi, R. Frederix, S. Frixione, M.V. Garzelli, F. Maltoni and R. Pittau, Automation of one-loop QCD corrections , JHEP 05

Original

2011

Cited alongside, same era.

M. Backes, A. Butter, T. Plehn and R. Winterhalder, How to GAN Event Unweighting , 2012.07873

Original