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.
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.
D. Derkach, N. Kazeev, F. Ratnikov, A. Ustyuzhanin and A. Volokhova, Cherenkov Detectors Fast Simulation Using Neural Networks , Nucl. Instrum. Meth. A 952
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.
A. Andreassen, P.T. Komiske, E.M. Metodiev, B. Nachman and J. Thaler, OmniFold: A Method to Simultaneously Unfold All Observables , Phys. Rev. Lett. 124
Original
1911
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.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn and R. Winterhalder, How to GAN away Detector Effects , SciPost Phys. 8
Original
1912
Earlier work this paper cites.
G. Altarelli and G. Parisi, Asymptotic freedom in parton language , Nuclear Physics B 126
1977
Earlier work this paper cites.
S. Badger, B. Biedermann, P. Uwer and V. Yundin, Numerical evaluation of virtual corrections to multi-jet production in massless QCD , Comput. Phys. Commun. 184
Original
1981
Earlier work this paper cites.
A. Bassetto, M. Ciafaloni and G. Marchesini, Jet structure and infrared sensitive quantities in perturbative qcd , Physics Reports 100
1983
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.
S. Catani, Y.L. Dokshitzer, M. Olsson, G. Turnock and B.R. Webber, New clustering algorithm for multi - jet cross-sections in e+ e- annihilation , Phys. Lett. B 269
1991
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.
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
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Z.-H. Zhou, J. Wu and W. Tang, Ensembling neural networks: Many could be better than all , Artificial Intelligence 137
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A. Buckley, A. Kvellestad, A. Raklev, P. Scott, J.V. Sparre, J. Van Den Abeele et al., Xsec: the cross-section evaluation code , Eur. Phys. J. C 80
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2006
Cited alongside, same era.
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Then

D.P. Kingma and J. Ba, Adam: A method for stochastic optimization , 2017
2017
Later among the works it cites.
M. Paganini, L. de Oliveira and B. Nachman, CaloGAN : Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks , Phys. Rev. D 97
Original
2018
Later among the works it cites.
L. Bottou, F.E. Curtis and J. Nocedal, Optimization methods for large-scale machine learning , 2018
2018
Later among the works it cites.
E. Bothmann and L. Debbio, Reweighting a parton shower using a neural network: the final-state case , JHEP 01
Original
2019
Later among the works it cites.
S. Otten, K. Rolbiecki, S. Caron, J.-S. Kim, R.R. de Austri and J. Tattersall, Deepxs: Fast approximation of mssm electroweak cross sections at nlo , 2019
2019

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Reading the bibliography…

2021

A factorisation-aware Matrix element emulator

Maître, Daniel, Truong, Henry

Understand

In this article we present a neural network based model to emulate matrix elements.

This model improves on existing methods by taking advantage of the known factorisation properties of matrix elements.
In so doing we can control the behaviour of simulated matrix elements when extrapolating into more singular regions than the ones used for training the neural network.
We apply our model to the case of leading-order jet production in $e^+e^-$ collisions with up to five jets.

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.
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.
D. Derkach, N. Kazeev, F. Ratnikov, A. Ustyuzhanin and A. Volokhova, Cherenkov Detectors Fast Simulation Using Neural Networks , Nucl. Instrum. Meth. A 952
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.
A. Andreassen, P.T. Komiske, E.M. Metodiev, B. Nachman and J. Thaler, OmniFold: A Method to Simultaneously Unfold All Observables , Phys. Rev. Lett. 124
Original
1911
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.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn and R. Winterhalder, How to GAN away Detector Effects , SciPost Phys. 8
Original
1912
Earlier work this paper cites.
G. Altarelli and G. Parisi, Asymptotic freedom in parton language , Nuclear Physics B 126
1977
Earlier work this paper cites.
S. Badger, B. Biedermann, P. Uwer and V. Yundin, Numerical evaluation of virtual corrections to multi-jet production in massless QCD , Comput. Phys. Commun. 184
Original
1981
Earlier work this paper cites.
A. Bassetto, M. Ciafaloni and G. Marchesini, Jet structure and infrared sensitive quantities in perturbative qcd , Physics Reports 100
1983
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.
S. Catani, Y.L. Dokshitzer, M. Olsson, G. Turnock and B.R. Webber, New clustering algorithm for multi - jet cross-sections in e+ e- annihilation , Phys. Lett. B 269
1991
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.
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.
Z.-H. Zhou, J. Wu and W. Tang, Ensembling neural networks: Many could be better than all , Artificial Intelligence 137
2002
Cited alongside, same era.
A. Buckley, A. Kvellestad, A. Raklev, P. Scott, J.V. Sparre, J. Van Den Abeele et al., Xsec: the cross-section evaluation code , Eur. Phys. J. C 80
Original
2006
Cited alongside, same era.
B. Nachman and J. Thaler, Neural resampler for Monte Carlo reweighting with preserved uncertainties , Phys. Rev. D 102

Then

D.P. Kingma and J. Ba, Adam: A method for stochastic optimization , 2017
2017
Later among the works it cites.
M. Paganini, L. de Oliveira and B. Nachman, CaloGAN : Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks , Phys. Rev. D 97
Original
2018
Later among the works it cites.
L. Bottou, F.E. Curtis and J. Nocedal, Optimization methods for large-scale machine learning , 2018
2018
Later among the works it cites.
E. Bothmann and L. Debbio, Reweighting a parton shower using a neural network: the final-state case , JHEP 01
Original
2019
Later among the works it cites.
S. Otten, K. Rolbiecki, S. Caron, J.-S. Kim, R.R. de Austri and J. Tattersall, Deepxs: Fast approximation of mssm electroweak cross sections at nlo , 2019
2019

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

Original

2007

Cited alongside, same era.

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

Original

2008

Cited alongside, same era.

Y. Alanazi et al., AI-based Monte Carlo event generator for electron-proton scattering , 2008.03151

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.

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

Original

2012

Cited alongside, same era.

M. Cacciari, G.P. Salam and G. Soyez, FastJet User Manual , Eur. Phys. J. C 72

Original

2012

Cited alongside, same era.