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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.
ATLAS, CMS, Addendum to the report on the physics at the HL-LHC, and perspectives for the HE-LHC: Collection of notes from ATLAS and CMS
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
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.
Y. Hao, A. Orlitsky, A. T. Suresh, and Y. Wu, Data amplification: A unified and competitive approach to property estimation
Original
1904
Earlier work this paper cites.
B. Axelrod, S. Garg, V. Sharan, and G. Valiant, Sample amplification: Increasing dataset size even when learning is impossible
Original
1904
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 and B. Nachman, Neural Networks for Full Phase-space Reweighting and Parameter Tuning
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.
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.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting
1929
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.

Similar

C. Gao, S. Höche, J. Isaacson, C. Krause, and H. Schulz, Event Generation with Normalizing Flows
Original
2001
Cited alongside, same era.
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.
K. T. Matchev and P. Shyamsundar, Uncertainties associated with GAN-generated datasets in high energy physics

Then

M. Paganini, L. de Oliveira, and B. Nachman, CaloGAN : Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks
Original
2018
Later among the works it cites.
P. Musella and F. Pandolfi, Fast and Accurate Simulation of Particle Detectors Using Generative Adversarial Networks
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
Original
2018
Later among the works it cites.
http://cds.cern.ch/record/2630433
ATLAS Collaboration, “Deep generative models for fast shower simulation in ATLAS.” ATL-SOFT-PUB-2018-001, 2018 · 2018
Later among the works it cites.
J. W. Monk, Deep Learning as a Parton Shower
Original

Beyond the bibliography

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

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Reading the bibliography…

2020

GANplifying Event Samples

Butter, Anja, Diefenbacher, Sascha, Kasieczka, Gregor et al.

Understand

A critical question concerning generative networks applied to event generation in particle physics is if the generated events add statistical precision beyond the training sample.

We show for a simple example with increasing dimensionality how generative networks indeed amplify the training statistics.
We quantify their impact through an amplification factor or equivalent numbers of sampled events.

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.
ATLAS, CMS, Addendum to the report on the physics at the HL-LHC, and perspectives for the HE-LHC: Collection of notes from ATLAS and CMS
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
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.
Y. Hao, A. Orlitsky, A. T. Suresh, and Y. Wu, Data amplification: A unified and competitive approach to property estimation
Original
1904
Earlier work this paper cites.
B. Axelrod, S. Garg, V. Sharan, and G. Valiant, Sample amplification: Increasing dataset size even when learning is impossible
Original
1904
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 and B. Nachman, Neural Networks for Full Phase-space Reweighting and Parameter Tuning
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.
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.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting
1929
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.

Similar

C. Gao, S. Höche, J. Isaacson, C. Krause, and H. Schulz, Event Generation with Normalizing Flows
Original
2001
Cited alongside, same era.
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.
K. T. Matchev and P. Shyamsundar, Uncertainties associated with GAN-generated datasets in high energy physics

Then

M. Paganini, L. de Oliveira, and B. Nachman, CaloGAN : Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks
Original
2018
Later among the works it cites.
P. Musella and F. Pandolfi, Fast and Accurate Simulation of Particle Detectors Using Generative Adversarial Networks
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
Original
2018
Later among the works it cites.
http://cds.cern.ch/record/2630433
ATLAS Collaboration, “Deep generative models for fast shower simulation in ATLAS.” ATL-SOFT-PUB-2018-001, 2018 · 2018
Later among the works it cites.
J. W. Monk, Deep Learning as a Parton Shower
Original

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

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.

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

Cited alongside, same era.

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

2005

Cited alongside, same era.

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.

MIT Press, Cambridge, MA, USA,

I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, Generative adversarial nets · 2014

Cited alongside, same era.

D. P. Kingma and M. Welling, Auto-encoding variational bayes

2014

Cited alongside, same era.

JMLR.org, 2015

D. J. Rezende and S. Mohamed, Variational inference with normalizing flows · 2015

Cited alongside, same era.

2018