Built on

C. Durkan, A. Bekasov, I. Murray and G. Papamakarios, Neural spline flows , in Advances in Neural Information Processing Systems , H. Wallach, H. Larochelle, A. Beygelzimer, F. Alché-Buc, E. Fox and R. Garnett, eds., vol. 32, Curran Associates, Inc., 2019, https://proceedings.neurips.cc/paper/2019/file/7ac71d433f282034e088473244df8c02-Paper.pdf [ 1906.04032 ]
Original
1906
Earlier work this paper cites.
Y. Song and S. Ermon, Generative modeling by estimating gradients of the data distribution , 1907.05600
Original
1907
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.
J.A. Gregory and R. Delbourgo, Piecewise Rational Quadratic Interpolation to Monotonic Data , IMA Journal of Numerical Analysis 2
1982
Earlier work this paper cites.
K.T. Matchev, A. Roman and P. Shyamsundar, Uncertainties associated with GAN-generated datasets in high energy physics , SciPost Phys. 12
Original
2002
Earlier work this paper cites.
S. Agostinelli, J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce et al., Geant4—a simulation toolkit , Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 506
2003
Earlier work this paper cites.
The ILD Concept Group, International Large Detector: Interim Design Report , 2003.01116
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.
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. Thomson, Particle flow calorimetry and the PandoraPFA algorithm , Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 611
2009
Earlier work this paper cites.
Y. Song, J. Sohl-Dickstein, D.P. Kingma, A. Kumar, S. Ermon and B. Poole, Score-based generative modeling through stochastic differential equations , 2011.13456
Original
2011
Earlier work this paper cites.
D.P. Kingma and M. Welling, Auto-Encoding Variational Bayes , arXiv e-prints (2013) [ 1312.6114 ]
Original
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.
D. Rezende and S. Mohamed, Variational inference with normalizing flows , in International conference on machine learning , pp. 1530–1538, PMLR, 2015
2015
Earlier work this paper cites.
M. Germain, K. Gregor, I. Murray and H. Larochelle, MADE: Masked Autoencoder for Distribution Estimation , arXiv e-prints (2015) [ 1502.03509 ]
Original
2015
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.
“iLCSoft Project Page.” https://github.com/iLCSoft , 2016
2016
Cited alongside, same era.
D.P. Kingma, T. Salimans, R. Jozefowicz, X. Chen, I. Sutskever and M. Welling, Improving Variational Inference with Inverse Autoregressive Flow , arXiv e-prints (2016) arXiv:1606.04934 [ 1606.04934 ]
Original
2016
Cited alongside, same era.
A. van den Oord, N. Kalchbrenner and K. Kavukcuoglu, Pixel recurrent neural networks , 2016
2016
Cited alongside, same era.
A. van den Oord, N. Kalchbrenner, O. Vinyals, L. Espeholt, A. Graves and K. Kavukcuoglu, Conditional image generation with pixelcnn decoders , 2016

Then

J. Ho, A. Jain and P. Abbeel, Denoising diffusion probabilistic models , Advances in Neural Information Processing Systems 33
2020
Later among the works it cites.
R. Yohay, The cms high granularity calorimeter for high luminosity lhc , Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 958
2020
Later among the works it cites.
10.5281/zenodo.4296287
C. Durkan, A. Bekasov, I. Murray and G. Papamakarios, nflows: normalizing flows in PyTorch , Nov., 2020 · 2020
Later among the works it cites.
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., Fast and accurate electromagnetic and hadronic showers from generative models , in EPJ Web of Conferences , vol. 251, p. 03049, EDP Sciences, 2021

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Reading the bibliography…

2023

L2LFlows: Generating High-Fidelity 3D Calorimeter Images

Diefenbacher, Sascha, Eren, Engin, Gaede, Frank et al.

Understand

We explore the use of normalizing flows to emulate Monte Carlo detector simulations of photon showers in a high-granularity electromagnetic calorimeter prototype for the International Large Detector (ILD).

Our proposed method -- which we refer to as "Layer-to-Layer-Flows" (L$2$LFlows) -- is an evolution of the CaloFlow architecture adapted to a higher-dimensional setting (30 layers of $10\times 10$ voxels each).
The main innovation of L$2$LFlows consists of introducing $30$ separate normalizing flows, one for each layer of the calorimeter, where each flow is conditioned on the previous five layers in order to learn the layer-to-layer correlations.
We compare our results to the BIB-AE, a state-of-the-art generative network trained on the same dataset and find our model has a significantly improved fidelity.

Built on

C. Durkan, A. Bekasov, I. Murray and G. Papamakarios, Neural spline flows , in Advances in Neural Information Processing Systems , H. Wallach, H. Larochelle, A. Beygelzimer, F. Alché-Buc, E. Fox and R. Garnett, eds., vol. 32, Curran Associates, Inc., 2019, https://proceedings.neurips.cc/paper/2019/file/7ac71d433f282034e088473244df8c02-Paper.pdf [ 1906.04032 ]
Original
1906
Earlier work this paper cites.
Y. Song and S. Ermon, Generative modeling by estimating gradients of the data distribution , 1907.05600
Original
1907
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.
J.A. Gregory and R. Delbourgo, Piecewise Rational Quadratic Interpolation to Monotonic Data , IMA Journal of Numerical Analysis 2
1982
Earlier work this paper cites.
K.T. Matchev, A. Roman and P. Shyamsundar, Uncertainties associated with GAN-generated datasets in high energy physics , SciPost Phys. 12
Original
2002
Earlier work this paper cites.
S. Agostinelli, J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce et al., Geant4—a simulation toolkit , Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 506
2003
Earlier work this paper cites.
The ILD Concept Group, International Large Detector: Interim Design Report , 2003.01116
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.
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. Thomson, Particle flow calorimetry and the PandoraPFA algorithm , Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 611
2009
Earlier work this paper cites.
Y. Song, J. Sohl-Dickstein, D.P. Kingma, A. Kumar, S. Ermon and B. Poole, Score-based generative modeling through stochastic differential equations , 2011.13456
Original
2011
Earlier work this paper cites.
D.P. Kingma and M. Welling, Auto-Encoding Variational Bayes , arXiv e-prints (2013) [ 1312.6114 ]
Original
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.
D. Rezende and S. Mohamed, Variational inference with normalizing flows , in International conference on machine learning , pp. 1530–1538, PMLR, 2015
2015
Earlier work this paper cites.
M. Germain, K. Gregor, I. Murray and H. Larochelle, MADE: Masked Autoencoder for Distribution Estimation , arXiv e-prints (2015) [ 1502.03509 ]
Original
2015
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.
“iLCSoft Project Page.” https://github.com/iLCSoft , 2016
2016
Cited alongside, same era.
D.P. Kingma, T. Salimans, R. Jozefowicz, X. Chen, I. Sutskever and M. Welling, Improving Variational Inference with Inverse Autoregressive Flow , arXiv e-prints (2016) arXiv:1606.04934 [ 1606.04934 ]
Original
2016
Cited alongside, same era.
A. van den Oord, N. Kalchbrenner and K. Kavukcuoglu, Pixel recurrent neural networks , 2016
2016
Cited alongside, same era.
A. van den Oord, N. Kalchbrenner, O. Vinyals, L. Espeholt, A. Graves and K. Kavukcuoglu, Conditional image generation with pixelcnn decoders , 2016

Then

J. Ho, A. Jain and P. Abbeel, Denoising diffusion probabilistic models , Advances in Neural Information Processing Systems 33
2020
Later among the works it cites.
R. Yohay, The cms high granularity calorimeter for high luminosity lhc , Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 958
2020
Later among the works it cites.
10.5281/zenodo.4296287
C. Durkan, A. Bekasov, I. Murray and G. Papamakarios, nflows: normalizing flows in PyTorch , Nov., 2020 · 2020
Later among the works it cites.
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., Fast and accurate electromagnetic and hadronic showers from generative models , in EPJ Web of Conferences , vol. 251, p. 03049, EDP Sciences, 2021

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

2016

Cited alongside, same era.

G. Papamakarios, T. Pavlakou and I. Murray, Masked Autoregressive Flow for Density Estimation , arXiv e-prints (2017) [ 1705.07057 ]

Original

2017

Cited alongside, same era.

T. Salimans, A. Karpathy, X. Chen and D.P. Kingma, Pixelcnn++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications , 2017

2017

Cited alongside, same era.

X. Chen, N. Mishra, M. Rohaninejad and P. Abbeel, Pixelsnail: An improved autoregressive generative model , 2017

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

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.

2021