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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.
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.
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.
E. Bothmann, T. Janßen, M. Knobbe, T. Schmale and S. Schumann, Exploring phase space with Neural Importance Sampling , SciPost Phys. 8
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.
C. Gao, J. Isaacson and C. Krause, i-flow: High-dimensional Integration and Sampling with Normalizing Flows , Mach. Learn. Sci. Tech. 1
Original
2001
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.
F.A. Di Bello, S. Ganguly, E. Gross, M. Kado, M. Pitt, L. Santi et al., Towards a Computer Vision Particle Flow , Eur. Phys. J. C 81
Original
2003
Earlier work this paper cites.
J. Brehmer and K. Cranmer, Flows for simultaneous manifold learning and density estimation , 2003.13913
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 , Comput. Softw. Big Sci. 5
Original
2005
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.
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.
J.D. Hunter, Matplotlib: A 2d graphics environment , Computing in Science Engineering 9
2007
Earlier work this paper cites.
HEP Software Foundation
Original
2008
Earlier work this paper cites.
S. Choi, J. Lim and H. Oh, Data-driven Estimation of Background Distribution through Neural Autoregressive Flows , 2008.03636
Original
2008
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
2011
Earlier work this paper cites.
P. Baldi, L. Blecher, A. Butter, J. Collado, J.N. Howard, F. Keilbach et al., How to GAN Higher Jet Resolution , SciPost Phys. 13
Original
2012
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.
A. Collette, Python and HDF5 , O’Reilly (2013)
2013
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.
D.P. Kingma and J. Ba, Adam: A method for stochastic optimization , 2014
2014
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.
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), 10.1142/9581
2015
Earlier work this paper cites.
C. Dong, C.C. Loy, K. He and X. Tang, Image super-resolution using deep convolutional networks , IEEE transactions on pattern analysis and machine intelligence 38
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.
10.48550/ARXIV.1505.05770
D.J. Rezende and S. Mohamed, Variational inference with normalizing flows , 2015 · 2015
Earlier work this paper cites.
J. Kim, J.K. Lee and K.M. Lee, Accurate image super-resolution using very deep convolutional networks , in Proceedings of the IEEE conference on computer vision and pattern recognition , pp. 1646–1654, 2016
2016
Earlier work this paper cites.

Similar

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.
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.
B. Lim, S. Son, H. Kim, S. Nah and K. Mu Lee, Enhanced deep residual networks for single image super-resolution , in Proceedings of the IEEE conference on computer vision and pattern recognition workshops , pp. 136–144, 2017
2017
Cited alongside, same era.
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta et al., Photo-realistic single image super-resolution using a generative adversarial network , in Proceedings of the IEEE conference on computer vision and pattern recognition , pp. 4681–4690, 2017
2017

Then

A. Abhishek, E. Drechsler, W. Fedorko and B. Stelzer, CaloDVAE : Discrete Variational Autoencoders for Fast Calorimeter Shower Simulation , 10, 2022 [ 2210.07430 ]
Original
2022
Later among the works it cites.
M.F. Giannelli, G. Kasieczka, C. Krause, B. Nachman, D. Salamani, D. Shih et al., “Fast calorimeter simulation challenge 2022 - dataset 2.” https://doi.org/10.5281/zenodo.6366271 , March, 2022
2022
Later among the works it cites.
M. Faucci Giannelli, G. Kasieczka, B. Nachman, D. Salamani, D. Shih and A. Zaborowska, “Fast calorimeter simulation challenge 2022.” https://calochallenge.github.io/homepage/ , 2022
2022
Later among the works it cites.
M.F. Giannelli, G. Kasieczka, C. Krause, B. Nachman, D. Salamani, D. Shih et al., “Fast calorimeter simulation challenge 2022 - dataset 1.” https://doi.org/10.5281/zenodo.6234054 , March, 2022
2022
Later among the works it cites.

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2023

Calorimeter shower superresolution

Pang, Ian, Raine, John Andrew, Shih, David

Understand

Calorimeter shower simulation is a major bottleneck in the Large Hadron Collider computational pipeline.

There have been recent efforts to employ deep-generative surrogate models to overcome this challenge.
However, many of best performing models have training and generation times that do not scale well to high-dimensional calorimeter showers.
In this work, we introduce SuperCalo, a flow-based superresolution model, and demonstrate that high-dimensional fine-grained calorimeter showers can be quickly upsampled from coarse-grained showers.

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.
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.
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.
E. Bothmann, T. Janßen, M. Knobbe, T. Schmale and S. Schumann, Exploring phase space with Neural Importance Sampling , SciPost Phys. 8
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.
C. Gao, J. Isaacson and C. Krause, i-flow: High-dimensional Integration and Sampling with Normalizing Flows , Mach. Learn. Sci. Tech. 1
Original
2001
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.
F.A. Di Bello, S. Ganguly, E. Gross, M. Kado, M. Pitt, L. Santi et al., Towards a Computer Vision Particle Flow , Eur. Phys. J. C 81
Original
2003
Earlier work this paper cites.
J. Brehmer and K. Cranmer, Flows for simultaneous manifold learning and density estimation , 2003.13913
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 , Comput. Softw. Big Sci. 5
Original
2005
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.
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.
J.D. Hunter, Matplotlib: A 2d graphics environment , Computing in Science Engineering 9
2007
Earlier work this paper cites.
HEP Software Foundation
Original
2008
Earlier work this paper cites.
S. Choi, J. Lim and H. Oh, Data-driven Estimation of Background Distribution through Neural Autoregressive Flows , 2008.03636
Original
2008
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
2011
Earlier work this paper cites.
P. Baldi, L. Blecher, A. Butter, J. Collado, J.N. Howard, F. Keilbach et al., How to GAN Higher Jet Resolution , SciPost Phys. 13
Original
2012
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.
A. Collette, Python and HDF5 , O’Reilly (2013)
2013
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.
D.P. Kingma and J. Ba, Adam: A method for stochastic optimization , 2014
2014
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.
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), 10.1142/9581
2015
Earlier work this paper cites.
C. Dong, C.C. Loy, K. He and X. Tang, Image super-resolution using deep convolutional networks , IEEE transactions on pattern analysis and machine intelligence 38
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.
10.48550/ARXIV.1505.05770
D.J. Rezende and S. Mohamed, Variational inference with normalizing flows , 2015 · 2015
Earlier work this paper cites.
J. Kim, J.K. Lee and K.M. Lee, Accurate image super-resolution using very deep convolutional networks , in Proceedings of the IEEE conference on computer vision and pattern recognition , pp. 1646–1654, 2016
2016
Earlier work this paper cites.

Similar

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.
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.
B. Lim, S. Son, H. Kim, S. Nah and K. Mu Lee, Enhanced deep residual networks for single image super-resolution , in Proceedings of the IEEE conference on computer vision and pattern recognition workshops , pp. 136–144, 2017
2017
Cited alongside, same era.
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta et al., Photo-realistic single image super-resolution using a generative adversarial network , in Proceedings of the IEEE conference on computer vision and pattern recognition , pp. 4681–4690, 2017
2017

Then

A. Abhishek, E. Drechsler, W. Fedorko and B. Stelzer, CaloDVAE : Discrete Variational Autoencoders for Fast Calorimeter Shower Simulation , 10, 2022 [ 2210.07430 ]
Original
2022
Later among the works it cites.
M.F. Giannelli, G. Kasieczka, C. Krause, B. Nachman, D. Salamani, D. Shih et al., “Fast calorimeter simulation challenge 2022 - dataset 2.” https://doi.org/10.5281/zenodo.6366271 , March, 2022
2022
Later among the works it cites.
M. Faucci Giannelli, G. Kasieczka, B. Nachman, D. Salamani, D. Shih and A. Zaborowska, “Fast calorimeter simulation challenge 2022.” https://calochallenge.github.io/homepage/ , 2022
2022
Later among the works it cites.
M.F. Giannelli, G. Kasieczka, C. Krause, B. Nachman, D. Salamani, D. Shih et al., “Fast calorimeter simulation challenge 2022 - dataset 1.” https://doi.org/10.5281/zenodo.6234054 , March, 2022
2022
Later among the works it cites.

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

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.

L.N. Smith, Cyclical learning rates for training neural networks , in 2017 IEEE winter conference on applications of computer vision (WACV) , pp. 464–472, IEEE, 2017

2017

Cited alongside, same era.

C. Guo, G. Pleiss, Y. Sun and K.Q. Weinberger, On Calibration of Modern Neural Networks , arXiv e-prints (2017) arXiv:1706.04599 [ 1706.04599 ]

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 , Physical Review D 97

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.

Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong and Y. Fu, Image super-resolution using very deep residual channel attention networks , in Proceedings of the European conference on computer vision (ECCV) , pp. 286–301, 2018

2018

Cited alongside, same era.

M.F. Giannelli, G. Kasieczka, C. Krause, B. Nachman, D. Salamani, D. Shih et al., “Fast calorimeter simulation challenge 2022 - dataset 3.” https://doi.org/10.5281/zenodo.6366324 , March, 2022

2022

Later among the works it cites.

A. Hallin, J. Isaacson, G. Kasieczka, C. Krause, B. Nachman, T. Quadfasel et al., Classifying anomalies through outer density estimation , Phys. Rev. D 106

Original

2022

Later among the works it cites.

P. Jawahar, T. Aarrestad, N. Chernyavskaya, M. Pierini, K.A. Wozniak, J. Ngadiuba et al., Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows , Front. Big Data 5

Original

2022

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