Built on

S. Otten, S. Caron, W. de Swart, M. van Beekveld, L. Hendriks, C. van Leeuwen, D. Podareanu, R. Ruiz de Austri, and R. Verheyen, Event Generation and Statistical Sampling for Physics with Deep Generative Models and a Density Information Buffer
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.
Sherpa Collaboration, Event Generation with Sherpa 2.2
Original
1905
Earlier work this paper cites.
C. Durkan, A. Bekasov, I. Murray, and G. Papamakarios, Neural spline flows
Original
1906
Earlier work this paper cites.
A. Butter, T. Plehn, and R. Winterhalder, How to GAN LHC Events
Original
1907
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
Original
1911
Earlier work this paper cites.
F. Bishara and M. Montull, (Machine) Learning Amplitudes for Faster Event Generation
Original
1912
Earlier work this paper cites.
D. Belayneh et al
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.
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.
W. H. Press and G. R. Farrar, Recursive stratified sampling for multidimensional monte carlo integration
1990
Earlier work this paper cites.
R. Kleiss and R. Pittau, Weight optimization in multichannel Monte Carlo
1994
Earlier work this paper cites.
T. Ohl, Vegas revisited: Adaptive Monte Carlo integration beyond factorization
1999
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.
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, S. Höche, J. Isaacson, C. Krause, and H. Schulz, Event Generation with Normalizing Flows
Original
2001
Earlier work this paper cites.
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
Earlier work this paper cites.
B. Nachman and D. Shih, Anomaly Detection with Density Estimation
Original
2001
Earlier work this paper cites.
S. Badger and J. Bullock, Using neural networks for efficient evaluation of high multiplicity scattering amplitudes
Original
2002
Earlier work this paper cites.
F. A. Di Bello, S. Ganguly, E. Gross, M. Kado, M. Pitt, L. Santi, and J. Shlomi, Towards a Computer Vision Particle Flow
Original
2003
Earlier work this paper cites.
F. Maltoni and T. Stelzer, MadEvent: Automatic event generation with MadGraph
2003
Earlier work this paper cites.
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
Earlier work this paper cites.

Similar

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
Original
2006
Cited alongside, same era.
A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman, and T. Plehn, GANplifying event samples
Original
2008
Cited alongside, same era.
I.-K. Chen, M. D. Klimek, and M. Perelstein, Improved Neural Network Monte Carlo Simulation
Original
2009
Cited alongside, same era.
K. Dohi, Variational Autoencoders for Jet Simulation
Original
2009
Cited alongside, same era.
G. P. Lepage, Adaptive multidimensional integration: VEGAS enhanced
Original

Then

J. Aylett-Bullock, S. Badger, and R. Moodie, Optimising simulations for diphoton production at hadron colliders using amplitude neural networks
Original
2021
Later among the works it cites.
D. Maître and H. Truong, A factorisation-aware Matrix element emulator
Original
2021
Later among the works it cites.
C. Chen, O. Cerri, T. Q. Nguyen, J. R. Vlimant, and M. Pierini, Analysis-Specific Fast Simulation at the LHC with Deep Learning
2021
Later among the works it cites.
A. Valassi, S. Roiser, O. Mattelaer, and S. Hageboeck, Design and engineering of a simplified workflow execution for the MG5aMC event generator on GPUs and vector CPUs
Original
2021
Later among the works it cites.
O. Mattelaer and K. Ostrolenk, Speeding up MadGraph5_aMC@NLO

Beyond the bibliography

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

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Reading the bibliography…

2023

The MadNIS Reloaded

Heimel, Theo, Huetsch, Nathan, Maltoni, Fabio et al.

Understand

In pursuit of precise and fast theory predictions for the LHC, we present an implementation of the MadNIS method in the MadGraph event generator.

A series of improvements in MadNIS further enhance its efficiency and speed.
We validate this implementation for realistic partonic processes and find significant gains from using modern machine learning in event generators.

Built on

S. Otten, S. Caron, W. de Swart, M. van Beekveld, L. Hendriks, C. van Leeuwen, D. Podareanu, R. Ruiz de Austri, and R. Verheyen, Event Generation and Statistical Sampling for Physics with Deep Generative Models and a Density Information Buffer
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.
Sherpa Collaboration, Event Generation with Sherpa 2.2
Original
1905
Earlier work this paper cites.
C. Durkan, A. Bekasov, I. Murray, and G. Papamakarios, Neural spline flows
Original
1906
Earlier work this paper cites.
A. Butter, T. Plehn, and R. Winterhalder, How to GAN LHC Events
Original
1907
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
Original
1911
Earlier work this paper cites.
F. Bishara and M. Montull, (Machine) Learning Amplitudes for Faster Event Generation
Original
1912
Earlier work this paper cites.
D. Belayneh et al
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.
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.
W. H. Press and G. R. Farrar, Recursive stratified sampling for multidimensional monte carlo integration
1990
Earlier work this paper cites.
R. Kleiss and R. Pittau, Weight optimization in multichannel Monte Carlo
1994
Earlier work this paper cites.
T. Ohl, Vegas revisited: Adaptive Monte Carlo integration beyond factorization
1999
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.
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, S. Höche, J. Isaacson, C. Krause, and H. Schulz, Event Generation with Normalizing Flows
Original
2001
Earlier work this paper cites.
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
Earlier work this paper cites.
B. Nachman and D. Shih, Anomaly Detection with Density Estimation
Original
2001
Earlier work this paper cites.
S. Badger and J. Bullock, Using neural networks for efficient evaluation of high multiplicity scattering amplitudes
Original
2002
Earlier work this paper cites.
F. A. Di Bello, S. Ganguly, E. Gross, M. Kado, M. Pitt, L. Santi, and J. Shlomi, Towards a Computer Vision Particle Flow
Original
2003
Earlier work this paper cites.
F. Maltoni and T. Stelzer, MadEvent: Automatic event generation with MadGraph
2003
Earlier work this paper cites.
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
Earlier work this paper cites.

Similar

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
Original
2006
Cited alongside, same era.
A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman, and T. Plehn, GANplifying event samples
Original
2008
Cited alongside, same era.
I.-K. Chen, M. D. Klimek, and M. Perelstein, Improved Neural Network Monte Carlo Simulation
Original
2009
Cited alongside, same era.
K. Dohi, Variational Autoencoders for Jet Simulation
Original
2009
Cited alongside, same era.
G. P. Lepage, Adaptive multidimensional integration: VEGAS enhanced
Original

Then

J. Aylett-Bullock, S. Badger, and R. Moodie, Optimising simulations for diphoton production at hadron colliders using amplitude neural networks
Original
2021
Later among the works it cites.
D. Maître and H. Truong, A factorisation-aware Matrix element emulator
Original
2021
Later among the works it cites.
C. Chen, O. Cerri, T. Q. Nguyen, J. R. Vlimant, and M. Pierini, Analysis-Specific Fast Simulation at the LHC with Deep Learning
2021
Later among the works it cites.
A. Valassi, S. Roiser, O. Mattelaer, and S. Hageboeck, Design and engineering of a simplified workflow execution for the MG5aMC event generator on GPUs and vector CPUs
Original
2021
Later among the works it cites.
O. Mattelaer and K. Ostrolenk, Speeding up MadGraph5_aMC@NLO

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

2009

Cited alongside, same era.

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

Original

2011

Cited alongside, same era.

W. Kilian, T. Ohl, and J. Reuter, WHIZARD: Simulating Multi-Particle Processes at LHC and ILC

Original

2011

Cited alongside, same era.

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

Original

2012

Cited alongside, same era.

P. Baldi, L. Blecher, A. Butter, J. Collado, J. N. Howard, F. Keilbach, T. Plehn, G. Kasieczka, and D. Whiteson, How to GAN Higher Jet Resolution

Original

2012

Cited alongside, same era.

S. Bieringer, A. Butter, T. Heimel, S. Höche, U. Köthe, T. Plehn, and S. T. Radev, Measuring QCD Splittings with Invertible Networks

Original

2012

Cited alongside, same era.

J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H. S. Shao, T. Stelzer, P. Torrielli, and M. Zaro, The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations

Original

2014

Cited alongside, same era.

F. Nielsen and R. Nock, On the chi square and higher-order chi distances for approximating f-divergences

Original

2014

Cited alongside, same era.

Original

2021