Built on

S. Marzani, G. Soyez, and M. Spannowsky, Looking inside jets: an introduction to jet substructure and boosted-object phenomenology , 1st ed., Lecture Notes in Physics, Vol. 958 (Springer, Cham, 2019) arXiv:1901.10342
Original
1901
Earlier work this paper cites.
H. Qu and L. Gouskos, ParticleNet: Jet Tagging via Particle Clouds, Phys. Rev. D 101
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.
T. Kynkäänniemi, T. Karras, S. Laine, J. Lehtinen, and T. Aila, Improved precision and recall metric for assessing generative models, in Advances in Neural Information Processing Systems , Vol. 32, edited by H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (Curran Associates, Inc., Red Hook, NY, 2019) 1904.06991
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.
M. J. Chong and D. Forsyth, Effectively unbiased FID and Inception score and where to find them, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020) arXiv:1911.07023
Original
1911
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.
R. T. D’Agnolo, G. Grosso, M. Pierini, A. Wulzer, and M. Zanetti, Learning Multivariate New Physics, Eur. Phys. J. C 81
Original
1912
Earlier work this paper cites.
S. Kullback and R. A. Leibler, On Information and Sufficiency, Ann. Stat. 22
1951
Earlier work this paper cites.
L. V. Kantorovich, Mathematical methods of organizing and planning production, Manage. Sci. 6
1960
Earlier work this paper cites.
S. Baker and R. D. Cousins, Clarification of the Use of Chi Square and Likelihood Functions in Fits to Histograms, Nucl. Instrum. Meth. 221
1984
Earlier work this paper cites.
A. K. C. Wong and M. You, Entropy and distance of random graphs with application to structural pattern recognition, IEEE Trans. Pattern Anal. Mach. Intell. 7
1985
Earlier work this paper cites.
J. Lin, Divergence measures based on the shannon entropy, IEEE Trans Inf. Theory 37
1991
Earlier work this paper cites.
J. K. Lindsey, Parametric Statistical Inference (Clarendon Press, 1996)
1996
Earlier work this paper cites.
A. Müller, Integral probability metrics and their generating classes of functions, Adv. Appl. Probab. 29
1997
Earlier work this paper cites.
D. Bertsimas and J. Tsitsiklis, Introduction to linear optimization (Athena Scientific, Nashua, NH, 1997)
1997
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.
GEANT4 Collaboration, GEANT4—a simulation toolkit, Nucl. Instrum. Meth. A 506
2003
Earlier work this paper cites.
J. H. Friedman, On multivariate goodness of fit and two sample testing, in PHYSTAT 2003 , Vol. C030908, edited by L. Lyons, R. P. Mount, and R. Reitmeyer (SLAC, Stanford, CA, 2003) p. THPD002
2003
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 , Vol. 19, edited by B. Schölkopf, J. Platt, and T. Hoffman (MIT Press, 2006) 0805.2368
Original
2006
Earlier work this paper cites.
S. Baffioni, C. Charlot, F. Ferri, D. Futyan, P. Meridiani, I. Puljak, C. Rovelli, R. Salerno, and Y. Sirois, Electron reconstruction in CMS , CMS Note CMS-NOTE-2006-040 (2006) http://cds.cern.ch/record/934070
2006
Earlier work this paper cites.

Similar

HEP Software Foundation, HL-LHC Computing Review: Common Tools and Community Software, in 2022 Snowmass Summer Study (2020) arXiv:2008.13636
Original
2008
Cited alongside, same era.
A. Butter and T. Plehn, Generative Networks for LHC events, in Artificial Intelligence for High Energy Physics , edited by P. Calafiura, D. Rousseau, and K. Terao (World Scientific, 2022) p. 191, arXiv:2008.08558
Original
2008
Cited alongside, same era.
C. Villani, Optimal Transport: Old and New , Grundlehren der mathematischen Wissenschaften, Vol. 338 (Springer Berlin, 2008)
2008
Cited alongside, same era.
B. K. Sriperumbudur, A. Gretton, K. Fukumizu, B. Schölkopf, and G. R. Lanckriet, Hilbert space embeddings and metrics on probability measures, J. Mach. Learn. Res. 11
2010
Cited alongside, same era.

Then

J. Lee, Y. Lee, J. Kim, A. Kosiorek, S. Choi, and Y. W. Teh, Set transformer: A framework for attention-based permutation-invariant neural networks, in Proceedings of the 36th International Conference on Machine Learning , Proceedings of Machine Learning Research, Vol. 97, edited by K. Chaudhuri and R. Salakhutdinov (PMLR, 2019) p. 3744
2019
Later among the works it cites.
F. Carminati, G. Khattak, V. Loncar, T. Q. Nguyen, M. Pierini, R. Brito Da Rocha, K. Samaras-Tsakiris, S. Vallecorsa, and J.-R. Vlimant, Generative Adversarial Networks for fast simulation, J. Phys. Conf. Ser. 1525
2020
Later among the works it cites.
C.-J. Simon-Gabriel, A. Barp, and L. W. Mackey, Metrizing weak convergence with maximum mean discrepancies, arXiv:2006.09268 (2020)
Original
2020
Later among the works it cites.
F. Liu, W. Xu, J. Lu, G. Zhang, A. Gretton, and D. J. Sutherland, Learning deep kernels for non-parametric two-sample tests, in Proceedings of the 37th International Conference on Machine Learning , Proceedings of Machine Learning Research, Vol. 119, edited by H. D. III and A. Singh (PMLR, 2020) pp. 6316–6326

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2022

Evaluating generative models in high energy physics

Kansal, Raghav, Li, Anni, Duarte, Javier et al.

Understand

There has been a recent explosion in research into machine-learning-based generative modeling to tackle computational challenges for simulations in high energy physics (HEP).

In order to use such alternative simulators in practice, we need well-defined metrics to compare different generative models and evaluate their discrepancy from the true distributions.
We present the first systematic review and investigation into evaluation metrics and their sensitivity to failure modes of generative models, using the framework of two-sample goodness-of-fit testing, and their relevance and viability for HEP.
Inspired by previous work in both physics and computer vision, we propose two new metrics, the Fr\'echet and kernel physics distances (FPD and KPD, respectively), and perform a variety of experiments measuring their performance on simple Gaussian-distributed, and simulated high energy jet datasets.

Built on

S. Marzani, G. Soyez, and M. Spannowsky, Looking inside jets: an introduction to jet substructure and boosted-object phenomenology , 1st ed., Lecture Notes in Physics, Vol. 958 (Springer, Cham, 2019) arXiv:1901.10342
Original
1901
Earlier work this paper cites.
H. Qu and L. Gouskos, ParticleNet: Jet Tagging via Particle Clouds, Phys. Rev. D 101
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.
T. Kynkäänniemi, T. Karras, S. Laine, J. Lehtinen, and T. Aila, Improved precision and recall metric for assessing generative models, in Advances in Neural Information Processing Systems , Vol. 32, edited by H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (Curran Associates, Inc., Red Hook, NY, 2019) 1904.06991
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.
M. J. Chong and D. Forsyth, Effectively unbiased FID and Inception score and where to find them, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020) arXiv:1911.07023
Original
1911
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.
R. T. D’Agnolo, G. Grosso, M. Pierini, A. Wulzer, and M. Zanetti, Learning Multivariate New Physics, Eur. Phys. J. C 81
Original
1912
Earlier work this paper cites.
S. Kullback and R. A. Leibler, On Information and Sufficiency, Ann. Stat. 22
1951
Earlier work this paper cites.
L. V. Kantorovich, Mathematical methods of organizing and planning production, Manage. Sci. 6
1960
Earlier work this paper cites.
S. Baker and R. D. Cousins, Clarification of the Use of Chi Square and Likelihood Functions in Fits to Histograms, Nucl. Instrum. Meth. 221
1984
Earlier work this paper cites.
A. K. C. Wong and M. You, Entropy and distance of random graphs with application to structural pattern recognition, IEEE Trans. Pattern Anal. Mach. Intell. 7
1985
Earlier work this paper cites.
J. Lin, Divergence measures based on the shannon entropy, IEEE Trans Inf. Theory 37
1991
Earlier work this paper cites.
J. K. Lindsey, Parametric Statistical Inference (Clarendon Press, 1996)
1996
Earlier work this paper cites.
A. Müller, Integral probability metrics and their generating classes of functions, Adv. Appl. Probab. 29
1997
Earlier work this paper cites.
D. Bertsimas and J. Tsitsiklis, Introduction to linear optimization (Athena Scientific, Nashua, NH, 1997)
1997
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.
GEANT4 Collaboration, GEANT4—a simulation toolkit, Nucl. Instrum. Meth. A 506
2003
Earlier work this paper cites.
J. H. Friedman, On multivariate goodness of fit and two sample testing, in PHYSTAT 2003 , Vol. C030908, edited by L. Lyons, R. P. Mount, and R. Reitmeyer (SLAC, Stanford, CA, 2003) p. THPD002
2003
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 , Vol. 19, edited by B. Schölkopf, J. Platt, and T. Hoffman (MIT Press, 2006) 0805.2368
Original
2006
Earlier work this paper cites.
S. Baffioni, C. Charlot, F. Ferri, D. Futyan, P. Meridiani, I. Puljak, C. Rovelli, R. Salerno, and Y. Sirois, Electron reconstruction in CMS , CMS Note CMS-NOTE-2006-040 (2006) http://cds.cern.ch/record/934070
2006
Earlier work this paper cites.

Similar

HEP Software Foundation, HL-LHC Computing Review: Common Tools and Community Software, in 2022 Snowmass Summer Study (2020) arXiv:2008.13636
Original
2008
Cited alongside, same era.
A. Butter and T. Plehn, Generative Networks for LHC events, in Artificial Intelligence for High Energy Physics , edited by P. Calafiura, D. Rousseau, and K. Terao (World Scientific, 2022) p. 191, arXiv:2008.08558
Original
2008
Cited alongside, same era.
C. Villani, Optimal Transport: Old and New , Grundlehren der mathematischen Wissenschaften, Vol. 338 (Springer Berlin, 2008)
2008
Cited alongside, same era.
B. K. Sriperumbudur, A. Gretton, K. Fukumizu, B. Schölkopf, and G. R. Lanckriet, Hilbert space embeddings and metrics on probability measures, J. Mach. Learn. Res. 11
2010
Cited alongside, same era.

Then

J. Lee, Y. Lee, J. Kim, A. Kosiorek, S. Choi, and Y. W. Teh, Set transformer: A framework for attention-based permutation-invariant neural networks, in Proceedings of the 36th International Conference on Machine Learning , Proceedings of Machine Learning Research, Vol. 97, edited by K. Chaudhuri and R. Salakhutdinov (PMLR, 2019) p. 3744
2019
Later among the works it cites.
F. Carminati, G. Khattak, V. Loncar, T. Q. Nguyen, M. Pierini, R. Brito Da Rocha, K. Samaras-Tsakiris, S. Vallecorsa, and J.-R. Vlimant, Generative Adversarial Networks for fast simulation, J. Phys. Conf. Ser. 1525
2020
Later among the works it cites.
C.-J. Simon-Gabriel, A. Barp, and L. W. Mackey, Metrizing weak convergence with maximum mean discrepancies, arXiv:2006.09268 (2020)
Original
2020
Later among the works it cites.
F. Liu, W. Xu, J. Lu, G. Zhang, A. Gretton, and D. J. Sutherland, Learning deep kernels for non-parametric two-sample tests, in Proceedings of the 37th International Conference on Machine Learning , Proceedings of Machine Learning Research, Vol. 119, edited by H. D. III and A. Singh (PMLR, 2020) pp. 6316–6326

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

ATLAS Collaboration (ATLAS), Expected photon performance in the ATLAS experiment , ATLAS Note ATL-PHYS-PUB-2011-007 (2011) https://cds.cern.ch/record/1345329

2011

Cited alongside, same era.

B. K. Sriperumbudur, K. Fukumizu, A. Gretton, B. Schölkopf, and G. R. G. Lanckriet, On the empirical estimation of integral probability metrics, Electron. J. Stat. 6

2012

Cited alongside, same era.

A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola, A kernel two-sample test, J. Mach. Learn. Res. 13

2012

Cited alongside, same era.

R. D. Cousins, Generalization of chisquare goodness-of-fit test for binned data using saturated models, with application to histograms (2013), http://www.physics.ucla.edu/~cousins/stats/cousins_saturated.pdf

2013

Cited alongside, same era.

R. Vanderbei, Linear Programming: Foundations and Extensions , International Series in Operations Research & Management Science (Springer US, 2013)

2013

Cited alongside, same era.

O. Brüning and L. Rossi, eds., The High Luminosity Large Hadron Collider: The new machine for illuminating the mysteries of Universe , Vol. 24 (World Scientific, 2015)

2015

Cited alongside, same era.

R. D. Cousins, On goodness–of–fit tests (2016), https://www.physics.ucla.edu/˜cousins/ stats/ongoodness6march2016.pdf

2016

Cited alongside, same era.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the inception architecture for computer vision, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) p. 2818, 1512.00567

Original

2016

Cited alongside, same era.

2020