Built on

C. Durkan, A. Bekasov, I. Murray and G. Papamakarios, Neural spline flows , Advances in Neural Information Processing Systems 32
Original
1906
Earlier work this paper cites.
SHiP Collaboration, Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks , 1909.04451
Original
1909
Earlier work this paper cites.
C. Winkler, D.E. Worrall, E. Hoogeboom and M. Welling, Learning likelihoods with conditional normalizing flows , CoRR abs/1912.00042
Original
1912
Earlier work this paper cites.
D. Belayneh et al., Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics , 1912.06794
Original
1912
Earlier work this paper cites.
M.P. Guthrie, R.G. Alsmiller and H.W. Bertini, Calculation of the capture of negative pions in light elements and comparison with experiments pertaining to cancer radiotherapy , Nucl. Instrum. Meth. 66
1968
Earlier work this paper cites.
H.W. Bertini and M.P. Guthrie, News item results from medium-energy intranuclear-cascade calculation , Nucl. Phys. A 169
1971
Earlier work this paper cites.
V.A. Karmanov, LIGHT FRONT WAVE FUNCTION OF RELATIVISTIC COMPOSITE SYSTEM IN EXPLICITLY SOLVABLE MODEL , Nucl. Phys. B 166
1980
Earlier work this paper cites.
B. Nilsson-Almqvist and E. Stenlund, Interactions Between Hadrons and Nuclei: The Lund Monte Carlo, Fritiof Version 1.6 , Comput. Phys. Commun. 43
1987
Earlier work this paper cites.
B. Andersson, G. Gustafson and B. Nilsson-Almqvist, A Model for Low p(t) Hadronic Reactions, with Generalizations to Hadron - Nucleus and Nucleus-Nucleus Collisions , Nucl. Phys. B 281
1987
Earlier work this paper cites.
B. Andersson, A. Tai and B.-H. Sa, Final state interactions in the (nuclear) FRITIOF string interaction scenario , Z. Phys. C 70
1996
Earlier work this paper cites.
B. Ganhuyag and V. Uzhinsky, Modified FRITIOF code: Negative charged particle production in high energy nucleus nucleus interactions , Czech. J. Phys. 47
1997
Earlier work this paper cites.
B. Nachman and D. Shih, Anomaly Detection with Density Estimation , Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
C. Gao, J. Isaacson and C. Krause, i-flow: High-Dimensional Integration and Sampling with Normalizing Flows , 2001.05486
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 , 2001.05478
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.
H. Burkhardt, V. Grichine, P. Gumplinger, V. Ivanchenko, R. Kokoulin, M. Maire et al., Geant4 standard electromagnetic package for hep applications , in IEEE Symposium Conference Record Nuclear Science 2004. , vol. 3, pp. 1907–1910 Vol. 3, 2004, DOI
2004
Earlier work this paper cites.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, A. Rousselot, R. Winterhalder et al., Invertible Networks or Partons to Detector and Back Again , SciPost Phys. 9
Original
2006
Earlier work this paper cites.
P. Kirichenko, P. Izmailov and A.G. Wilson, Why Normalizing Flows Fail to Detect Out-of-Distribution Data , arXiv e-prints (2020) arXiv:2006.08545 [ 2006.08545 ]
Original
2006
Earlier work this paper cites.
J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce Dubois, M. Asai et al., Geant4 developments and applications , IEEE Transactions on Nuclear Science 53
2006
Earlier work this paper cites.
B. Stienen and R. Verheyen, Phase space sampling and inference from weighted events with autoregressive flows , SciPost Phys. 10
Original
2011
Earlier work this paper cites.
S. Bieringer, A. Butter, T. Heimel, S. Höche, U. Köthe, T. Plehn et al., Measuring QCD Splittings with Invertible Networks , SciPost Phys. 10
Original
2012
Earlier work this paper cites.
E.G. Tabak and C.V. Turner, A family of nonparametric density estimation algorithms , Communications on Pure and Applied Mathematics 66
2013
Earlier work this paper cites.
L. Dinh, D. Krueger and Y. Bengio, Nice: Non-linear independent components estimation , arXiv preprint arXiv:1410.8516 (2014)
Original
2014
Earlier work this paper cites.
D.P. Kingma and J. Ba, Adam: A method for stochastic optimization , 2014
2014
Earlier work this paper cites.
10.48550/ARXIV.1505.05770
D.J. Rezende and S. Mohamed, Variational inference with normalizing flows , 2015 · 2015
Earlier work this paper cites.
M. Germain, K. Gregor, I. Murray and H. Larochelle, Made: Masked autoencoder for distribution estimation , in International conference on machine learning , pp. 881–889, PMLR, 2015
2015
Earlier work this paper cites.

Similar

L. Dinh, J. Sohl-Dickstein and S. Bengio, Density estimation using real nvp , arXiv preprint arXiv:1605.08803 (2016)
Original
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.
B. Nachman, L. de Oliveira and M. Paganini, Electromagnetic calorimeter shower images , Mendeley Data (2017)
2017
Cited alongside, same era.
G. Papamakarios, T. Pavlakou and I. Murray, Masked autoregressive flow for density estimation , Advances in neural information processing systems 30
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

Then

E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol et al., Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network , EPJ Web Conf. 251
Original
2021
Later among the works it cites.
E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, D. Hundhausen, G. Kasieczka et al., Hadrons, Better, Faster, Stronger , Mach.Learn.Sci.Tech. 3
Original
2021
Later among the works it cites.
C. Le Lan and L. Dinh, Perfect density models cannot guarantee anomaly detection , Entropy 23
2021
Later among the works it cites.
R. Winterhalder, V. Magerya, E. Villa, S.P. Jones, M. Kerner, A. Butter et al., Targeting Multi-Loop Integrals with Neural Networks , SciPost Phys. 12
Original
2022
Later among the works it cites.
A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman, T. Plehn, D. Shih et al., Ephemeral Learning – Augmenting Triggers with Online-Trained Normalizing Flows , SciPost Phys. 13

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Reading the bibliography…

2023

Anomaly detection with flow-based fast calorimeter simulators

Krause, Claudius, Nachman, Benjamin, Pang, Ian et al.

Understand

Recently, several normalizing flow-based deep generative models have been proposed to accelerate the simulation of calorimeter showers.

Using CaloFlow as an example, we show that these models can simultaneously perform unsupervised anomaly detection with no additional training cost.
As a demonstration, we consider electromagnetic showers initiated by one (background) or multiple (signal) photons.
The CaloFlow model is designed to generate single photon showers, but it also provides access to the shower likelihood.

Built on

C. Durkan, A. Bekasov, I. Murray and G. Papamakarios, Neural spline flows , Advances in Neural Information Processing Systems 32
Original
1906
Earlier work this paper cites.
SHiP Collaboration, Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks , 1909.04451
Original
1909
Earlier work this paper cites.
C. Winkler, D.E. Worrall, E. Hoogeboom and M. Welling, Learning likelihoods with conditional normalizing flows , CoRR abs/1912.00042
Original
1912
Earlier work this paper cites.
D. Belayneh et al., Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics , 1912.06794
Original
1912
Earlier work this paper cites.
M.P. Guthrie, R.G. Alsmiller and H.W. Bertini, Calculation of the capture of negative pions in light elements and comparison with experiments pertaining to cancer radiotherapy , Nucl. Instrum. Meth. 66
1968
Earlier work this paper cites.
H.W. Bertini and M.P. Guthrie, News item results from medium-energy intranuclear-cascade calculation , Nucl. Phys. A 169
1971
Earlier work this paper cites.
V.A. Karmanov, LIGHT FRONT WAVE FUNCTION OF RELATIVISTIC COMPOSITE SYSTEM IN EXPLICITLY SOLVABLE MODEL , Nucl. Phys. B 166
1980
Earlier work this paper cites.
B. Nilsson-Almqvist and E. Stenlund, Interactions Between Hadrons and Nuclei: The Lund Monte Carlo, Fritiof Version 1.6 , Comput. Phys. Commun. 43
1987
Earlier work this paper cites.
B. Andersson, G. Gustafson and B. Nilsson-Almqvist, A Model for Low p(t) Hadronic Reactions, with Generalizations to Hadron - Nucleus and Nucleus-Nucleus Collisions , Nucl. Phys. B 281
1987
Earlier work this paper cites.
B. Andersson, A. Tai and B.-H. Sa, Final state interactions in the (nuclear) FRITIOF string interaction scenario , Z. Phys. C 70
1996
Earlier work this paper cites.
B. Ganhuyag and V. Uzhinsky, Modified FRITIOF code: Negative charged particle production in high energy nucleus nucleus interactions , Czech. J. Phys. 47
1997
Earlier work this paper cites.
B. Nachman and D. Shih, Anomaly Detection with Density Estimation , Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
C. Gao, J. Isaacson and C. Krause, i-flow: High-Dimensional Integration and Sampling with Normalizing Flows , 2001.05486
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 , 2001.05478
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.
H. Burkhardt, V. Grichine, P. Gumplinger, V. Ivanchenko, R. Kokoulin, M. Maire et al., Geant4 standard electromagnetic package for hep applications , in IEEE Symposium Conference Record Nuclear Science 2004. , vol. 3, pp. 1907–1910 Vol. 3, 2004, DOI
2004
Earlier work this paper cites.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, A. Rousselot, R. Winterhalder et al., Invertible Networks or Partons to Detector and Back Again , SciPost Phys. 9
Original
2006
Earlier work this paper cites.
P. Kirichenko, P. Izmailov and A.G. Wilson, Why Normalizing Flows Fail to Detect Out-of-Distribution Data , arXiv e-prints (2020) arXiv:2006.08545 [ 2006.08545 ]
Original
2006
Earlier work this paper cites.
J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce Dubois, M. Asai et al., Geant4 developments and applications , IEEE Transactions on Nuclear Science 53
2006
Earlier work this paper cites.
B. Stienen and R. Verheyen, Phase space sampling and inference from weighted events with autoregressive flows , SciPost Phys. 10
Original
2011
Earlier work this paper cites.
S. Bieringer, A. Butter, T. Heimel, S. Höche, U. Köthe, T. Plehn et al., Measuring QCD Splittings with Invertible Networks , SciPost Phys. 10
Original
2012
Earlier work this paper cites.
E.G. Tabak and C.V. Turner, A family of nonparametric density estimation algorithms , Communications on Pure and Applied Mathematics 66
2013
Earlier work this paper cites.
L. Dinh, D. Krueger and Y. Bengio, Nice: Non-linear independent components estimation , arXiv preprint arXiv:1410.8516 (2014)
Original
2014
Earlier work this paper cites.
D.P. Kingma and J. Ba, Adam: A method for stochastic optimization , 2014
2014
Earlier work this paper cites.
10.48550/ARXIV.1505.05770
D.J. Rezende and S. Mohamed, Variational inference with normalizing flows , 2015 · 2015
Earlier work this paper cites.
M. Germain, K. Gregor, I. Murray and H. Larochelle, Made: Masked autoencoder for distribution estimation , in International conference on machine learning , pp. 881–889, PMLR, 2015
2015
Earlier work this paper cites.

Similar

L. Dinh, J. Sohl-Dickstein and S. Bengio, Density estimation using real nvp , arXiv preprint arXiv:1605.08803 (2016)
Original
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.
B. Nachman, L. de Oliveira and M. Paganini, Electromagnetic calorimeter shower images , Mendeley Data (2017)
2017
Cited alongside, same era.
G. Papamakarios, T. Pavlakou and I. Murray, Masked autoregressive flow for density estimation , Advances in neural information processing systems 30
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

Then

E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol et al., Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network , EPJ Web Conf. 251
Original
2021
Later among the works it cites.
E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, D. Hundhausen, G. Kasieczka et al., Hadrons, Better, Faster, Stronger , Mach.Learn.Sci.Tech. 3
Original
2021
Later among the works it cites.
C. Le Lan and L. Dinh, Perfect density models cannot guarantee anomaly detection , Entropy 23
2021
Later among the works it cites.
R. Winterhalder, V. Magerya, E. Villa, S.P. Jones, M. Kerner, A. Butter et al., Targeting Multi-Loop Integrals with Neural Networks , SciPost Phys. 12
Original
2022
Later among the works it cites.
A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman, T. Plehn, D. Shih et al., Ephemeral Learning – Augmenting Triggers with Online-Trained Normalizing Flows , SciPost Phys. 13

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

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.

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.

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.

ATLAS Collaboration, Deep generative models for fast shower simulation in ATLAS , ATL-SOFT-PUB-2018-001 (2018)

2018

Cited alongside, same era.

V. Chekalina, E. Orlova, F. Ratnikov, D. Ulyanov, A. Ustyuzhanin and E. Zakharov, Generative Models for Fast Calorimeter Simulation.LHCb case , CHEP 2018 (2018) [ 1812.01319 ]

Original

2018

Cited alongside, same era.

F. Carminati, A. Gheata, G. Khattak, P. Mendez Lorenzo, S. Sharan and S. Vallecorsa, Three dimensional Generative Adversarial Networks for fast simulation , Proceedings, 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017): Seattle, WA, USA, August 21-25, 2017; J. Phys. Conf. Ser. 1085

2018

Cited alongside, same era.

S. Vallecorsa, Generative models for fast simulation , Proceedings, 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017): Seattle, WA, USA, August 21-25, 2017; J. Phys. Conf. Ser. 1085

2018

Cited alongside, same era.

Original

2022

Later among the works it cites.

R. Verheyen, Event Generation and Density Estimation with Surjective Normalizing Flows , SciPost Phys. 13

Original

2022

Later among the works it cites.

M. Leigh, J.A. Raine and T. Golling, ν \nu -Flows: conditional neutrino regression , SciPost Phys. 14

Original

2022