Built on

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.
Y. Hao, A. Orlitsky, A. T. Suresh and Y. Wu, Data amplification: A unified and competitive approach to property estimation , 1904.00070
Original
1904
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.
D. Belayneh et al., Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics , Eur. Phys. J. C 80
Original
1912
Earlier work this paper cites.
S. Krippendorf and M. Syvaeri, Detecting Symmetries with Neural Networks , 2003.13679 LMU-ASC 11/20, MPP-2020-34, [ 2003.13679 ]
Original
2003
Earlier work this paper cites.
E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol et al., Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed , 2005.05334 DESY-20-075, [ 2005.05334 ]
Original
2005
Earlier work this paper cites.
A. Gretton, K. Borgwardt, M. Rasch, B. Schölkopf and A. Smola, A kernel method for the two-sample-problem , in Advances in Neural Information Processing Systems , B. Schölkopf, J. Platt and T. Hoffman, eds., vol. 19, MIT Press, 2007, https://proceedings.neurips.cc/paper/2006/file/e9fb2eda3d9c55a0d89c98d6c54b5b3e-Paper.pdf
2006
Earlier work this paper cites.
A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman and T. Plehn, GANplifying event samples , SciPost Phys. 10
Original
2008
Earlier work this paper cites.
M. A. Thomson, Particle flow calorimetry and the PandoraPFA algorithm , Nuclear Instruments and Methods in Physics Research A 611
Original
2009
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel et al., Scikit-learn: Machine learning in Python , Journal of Machine Learning Research 12
Original
2011
Earlier work this paper cites.
M. Backes, A. Butter, T. Plehn and R. Winterhalder, How to GAN Event Unweighting , 2012.07873
Original
2012
Earlier work this paper cites.
A. L. Maas, A. Y. Hannun and A. Y. Ng, Rectifier nonlinearities improve neural network acoustic models , in in ICML Workshop on Deep Learning for Audio, Speech and Language Processing , 2013
2013
Earlier work this paper cites.
M. Frank, F. Gaede, C. Grefe and P. Mato, DD4hep: A Detector Description Toolkit for High Energy Physics Experiments , J. Phys. Conf. Ser. 513
2014
Earlier work this paper cites.

Similar

J. S. Marshall and M. A. Thomson, The Pandora Software Development Kit for Pattern Recognition , Eur. Phys. J. C 75
Original
2015
Cited alongside, same era.
“iLCSoft Project Page.” https://github.com/iLCSoft , 2016
2016
Cited alongside, same era.
J. Allison, K. Amako, J. Apostolakis, P. Arce, M. Asai, T. Aso et al., Recent developments in geant4 , Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 835
2016
Cited alongside, same era.
A. B. L. Larsen, S. K. Sønderby, H. Larochelle and O. Winther, Autoencoding beyond pixels using a learned similarity metric , in Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48 , p. 1558–1566, JMLR.org, 2016
2016
Cited alongside, same era.

Then

T. Miyato, T. Kataoka, M. Koyama and Y. Yoshida, Spectral Normalization for Generative Adversarial Networks , arXiv e-prints (2018) arXiv:1802.05957 [ 1802.05957 ]
Original
2018
Later among the works it cites.
G. Biau, B. Cadre, M. Sangnier and U. Tanielian, Some theoretical properties of gans , Annals of Statistics 48
Original
2018
Later among the works it cites.
S. Vallecorsa, F. Carminati and G. Khattak, 3D convolutional GAN for fast simulation , EPJ Web Conf. 214
2019
Later among the works it cites.
V. Chekalina, E. Orlova, F. Ratnikov, D. Ulyanov, A. Ustyuzhanin and E. Zakharov, Generative Models for Fast Calorimeter Simulation: the LHCb case , EPJ Web Conf. 214
Original
2019
Later among the works it cites.
K. Deja, T. Trzcinski and L. Graczykowski, Generative models for fast cluster simulations in the tpc for the alice experiment , EPJ Web Conf. 214

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Reading the bibliography…

2022

Calomplification -- The Power of Generative Calorimeter Models

Bieringer, Sebastian, Butter, Anja, Diefenbacher, Sascha et al.

Understand

Motivated by the high computational costs of classical simulations, machine-learned generative models can be extremely useful in particle physics and elsewhere.

They become especially attractive when surrogate models can efficiently learn the underlying distribution, such that a generated sample outperforms a training sample of limited size.
This kind of GANplification has been observed for simple Gaussian models.
We show the same effect for a physics simulation, specifically photon showers in an electromagnetic calorimeter.

Built on

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.
Y. Hao, A. Orlitsky, A. T. Suresh and Y. Wu, Data amplification: A unified and competitive approach to property estimation , 1904.00070
Original
1904
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.
D. Belayneh et al., Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics , Eur. Phys. J. C 80
Original
1912
Earlier work this paper cites.
S. Krippendorf and M. Syvaeri, Detecting Symmetries with Neural Networks , 2003.13679 LMU-ASC 11/20, MPP-2020-34, [ 2003.13679 ]
Original
2003
Earlier work this paper cites.
E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol et al., Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed , 2005.05334 DESY-20-075, [ 2005.05334 ]
Original
2005
Earlier work this paper cites.
A. Gretton, K. Borgwardt, M. Rasch, B. Schölkopf and A. Smola, A kernel method for the two-sample-problem , in Advances in Neural Information Processing Systems , B. Schölkopf, J. Platt and T. Hoffman, eds., vol. 19, MIT Press, 2007, https://proceedings.neurips.cc/paper/2006/file/e9fb2eda3d9c55a0d89c98d6c54b5b3e-Paper.pdf
2006
Earlier work this paper cites.
A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman and T. Plehn, GANplifying event samples , SciPost Phys. 10
Original
2008
Earlier work this paper cites.
M. A. Thomson, Particle flow calorimetry and the PandoraPFA algorithm , Nuclear Instruments and Methods in Physics Research A 611
Original
2009
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel et al., Scikit-learn: Machine learning in Python , Journal of Machine Learning Research 12
Original
2011
Earlier work this paper cites.
M. Backes, A. Butter, T. Plehn and R. Winterhalder, How to GAN Event Unweighting , 2012.07873
Original
2012
Earlier work this paper cites.
A. L. Maas, A. Y. Hannun and A. Y. Ng, Rectifier nonlinearities improve neural network acoustic models , in in ICML Workshop on Deep Learning for Audio, Speech and Language Processing , 2013
2013
Earlier work this paper cites.
M. Frank, F. Gaede, C. Grefe and P. Mato, DD4hep: A Detector Description Toolkit for High Energy Physics Experiments , J. Phys. Conf. Ser. 513
2014
Earlier work this paper cites.

Similar

J. S. Marshall and M. A. Thomson, The Pandora Software Development Kit for Pattern Recognition , Eur. Phys. J. C 75
Original
2015
Cited alongside, same era.
“iLCSoft Project Page.” https://github.com/iLCSoft , 2016
2016
Cited alongside, same era.
J. Allison, K. Amako, J. Apostolakis, P. Arce, M. Asai, T. Aso et al., Recent developments in geant4 , Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 835
2016
Cited alongside, same era.
A. B. L. Larsen, S. K. Sønderby, H. Larochelle and O. Winther, Autoencoding beyond pixels using a learned similarity metric , in Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48 , p. 1558–1566, JMLR.org, 2016
2016
Cited alongside, same era.

Then

T. Miyato, T. Kataoka, M. Koyama and Y. Yoshida, Spectral Normalization for Generative Adversarial Networks , arXiv e-prints (2018) arXiv:1802.05957 [ 1802.05957 ]
Original
2018
Later among the works it cites.
G. Biau, B. Cadre, M. Sangnier and U. Tanielian, Some theoretical properties of gans , Annals of Statistics 48
Original
2018
Later among the works it cites.
S. Vallecorsa, F. Carminati and G. Khattak, 3D convolutional GAN for fast simulation , EPJ Web Conf. 214
2019
Later among the works it cites.
V. Chekalina, E. Orlova, F. Ratnikov, D. Ulyanov, A. Ustyuzhanin and E. Zakharov, Generative Models for Fast Calorimeter Simulation: the LHCb case , EPJ Web Conf. 214
Original
2019
Later among the works it cites.
K. Deja, T. Trzcinski and L. Graczykowski, Generative models for fast cluster simulations in the tpc for the alice experiment , EPJ Web Conf. 214

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, X. Chen et al., Improved techniques for training gans , in Advances in Neural Information Processing Systems , D. Lee, M. Sugiyama, U. Luxburg, I. Guyon and R. Garnett, eds., vol. 29, Curran Associates, Inc., 2016, https://proceedings.neurips.cc/paper/2016/file/8a3363abe792db2d8761d6403605aeb7-Paper.pdf

2016

Cited alongside, same era.

L. de Oliveira, M. Paganini and B. Nachman, Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis , Comput. Softw. Big Sci. 1

Original

2017

Cited alongside, same era.

M. Paganini, L. de Oliveira and B. Nachman, Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters , Phys. Rev. Lett. 120

Original

2018

Cited alongside, same era.

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. D97

Original

2018

Cited alongside, same era.

P. Musella and F. Pandolfi, Fast and Accurate Simulation of Particle Detectors Using Generative Adversarial Networks , Comput. Softw. Big Sci. 2

Original

2018

Cited alongside, same era.

L. de Oliveira, M. Paganini and B. Nachman, Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters , J. Phys. Conf. Ser. 1085

Original

2018

Cited alongside, same era.

J. W. Monk, Deep Learning as a Parton Shower , JHEP 12

Original

2018

Cited alongside, same era.

M. Erdmann, L. Geiger, J. Glombitza and D. Schmidt, Generating and refining particle detector simulations using the Wasserstein distance in adversarial networks , Comput. Softw. Big Sci. 2

Original

2018

Cited alongside, same era.

2019