Built on

Event Generation and Statistical Sampling for Physics with Deep Generative Models and a Density Information Buffer ,
Original
S. Otten, S. Caron, W. de Swart, M. van Beekveld, L. Hendriks, C. van Leeuwen, D. Podareanu, R. Ruiz de Austri and R. Verheyen, · 1901
Earlier work this paper cites.
LHC analysis-specific datasets with Generative Adversarial Networks (2019),
Original
B. Hashemi, N. Amin, K. Datta, D. Olivito and M. Pierini, · 1901
Earlier work this paper cites.
ParticleNet: Jet Tagging via Particle Clouds ,
Original
H. Qu and L. Gouskos, · 1902
Earlier work this paper cites.
DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC ,
Original
R. Di Sipio, M. Faucci Giannelli, S. Ketabchi Haghighat and S. Palazzo, · 1903
Earlier work this paper cites.
How to GAN LHC Events ,
Original
A. Butter, T. Plehn and R. Winterhalder, · 1907
Earlier work this paper cites.
OmniFold: A Method to Simultaneously Unfold All Observables ,
Original
A. Andreassen, P. T. Komiske, E. M. Metodiev, B. Nachman and J. Thaler, · 1911
Earlier work this paper cites.
How to GAN Event Subtraction (2019),
Original
A. Butter, T. Plehn and R. Winterhalder, · 1912
Earlier work this paper cites.
Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics ,
Original
D. Belayneh et al. , · 1912
Earlier work this paper cites.
How to GAN away Detector Effects ,
Original
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn and R. Winterhalder, · 1912
Earlier work this paper cites.
Exploring phase space with Neural Importance Sampling ,
Original
E. Bothmann, T. Janßen, M. Knobbe, T. Schmale and S. Schumann, · 2001
Earlier work this paper cites.
i-flow: High-dimensional Integration and Sampling with Normalizing Flows ,
Original
C. Gao, J. Isaacson and C. Krause, · 2001
Earlier work this paper cites.
Event Generation with Normalizing Flows ,
Original
C. Gao, S. Höche, J. Isaacson, C. Krause and H. Schulz, · 2001
Earlier work this paper cites.
Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN) (2020),
Original
Y. Alanazi, N. Sato, T. Liu, W. Melnitchouk, M. P. Kuchera, E. Pritchard, M. Robertson, R. Strauss, L. Velasco and Y. Li, · 2001
Earlier work this paper cites.
Anomaly Detection with Density Estimation ,
Original
B. Nachman and D. Shih, · 2001
Earlier work this paper cites.
On the differences between calorimetric detection of electrons and photons ,
R. Wigmans and M. T. Zeyrek, · 2002
Earlier work this paper cites.
Towards a Computer Vision Particle Flow ,
Original
F. A. Di Bello, S. Ganguly, E. Gross, M. Kado, M. Pitt, L. Santi and J. Shlomi, · 2003
Earlier work this paper cites.
The tradeoff between generative and discriminative classifiers ,
G. Bouchard and B. Triggs, · 2004
Earlier work this paper cites.
Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed ,
Original
E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol and K. Krüger, · 2005
Earlier work this paper cites.
Invertible Networks or Partons to Detector and Back Again ,
Original
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, A. Rousselot, R. Winterhalder, L. Ardizzone and U. Köthe, · 2006
Earlier work this paper cites.
GANplifying event samples ,
Original
A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman and T. Plehn, · 2008
Earlier work this paper cites.
Improved Neural Network Monte Carlo Simulation ,
Original
I.-K. Chen, M. D. Klimek and M. Perelstein, · 2009
Earlier work this paper cites.
Variational Autoencoders for Jet Simulation (2020),
Original
K. Dohi, · 2009
Earlier work this paper cites.
DCTRGAN: Improving the Precision of Generative Models with Reweighting ,
Original
S. Diefenbacher, E. Eren, G. Kasieczka, A. Korol, B. Nachman and D. Shih, · 2009
Earlier work this paper cites.
Mapping Machine-Learned Physics into a Human-Readable Space ,
Original
T. Faucett, J. Thaler and D. Whiteson, · 2010
Earlier work this paper cites.
Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows ,
Original
B. Stienen and R. Verheyen, · 2011
Earlier work this paper cites.
How to GAN Event Unweighting ,
Original
M. Backes, A. Butter, T. Plehn and R. Winterhalder, · 2012
Earlier work this paper cites.
How to GAN Higher Jet Resolution ,
Original
P. Baldi, L. Blecher, A. Butter, J. Collado, J. N. Howard, F. Keilbach, T. Plehn, G. Kasieczka and D. Whiteson, · 2012
Earlier work this paper cites.
Measuring QCD Splittings with Invertible Networks ,
Original
S. Bieringer, A. Butter, T. Heimel, S. Höche, U. Köthe, T. Plehn and S. T. Radev, · 2012
Earlier work this paper cites.
Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis ,
Original
L. de Oliveira, M. Paganini and B. Nachman, · 2017
Earlier work this paper cites.

Similar

Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters ,
Original
M. Paganini, L. de Oliveira and B. Nachman, · 2018
Cited alongside, same era.
Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters ,
Original
L. de Oliveira, M. Paganini and B. Nachman, · 2018
Cited alongside, same era.
CaloGAN : Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks ,
Original
M. Paganini, L. de Oliveira and B. Nachman, · 2018
Cited alongside, same era.
Generating and refining particle detector simulations using the Wasserstein distance in adversarial networks ,
Original
M. Erdmann, L. Geiger, J. Glombitza and D. Schmidt, · 2018
Cited alongside, same era.

Then

Calomplification — the power of generative calorimeter models ,
Original
S. Bieringer, A. Butter, S. Diefenbacher, E. Eren, F. Gaede, D. Hundhausen, G. Kasieczka, B. Nachman, T. Plehn and M. Trabs, · 2022
Later among the works it cites.
An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative training (2022),
Original
M. Backes, A. Butter, M. Dunford and B. Malaescu, · 2022
Later among the works it cites.
Two Invertible Networks for the Matrix Element Method (2022),
Original
A. Butter, T. Heimel, T. Martini, S. Peitzsch and T. Plehn, · 2022
Later among the works it cites.
Classifying anomalies through outer density estimation ,
Original
A. Hallin, J. Isaacson, G. Kasieczka, C. Krause, B. Nachman, T. Quadfasel, M. Schlaffer, D. Shih and M. Sommerhalder, · 2022
Later among the works it cites.

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

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Reading the bibliography…

2023

How to Understand Limitations of Generative Networks

Das, Ranit, Favaro, Luigi, Heimel, Theo et al.

Understand

Well-trained classifiers and their complete weight distributions provide us with a well-motivated and practicable method to test generative networks in particle physics.

We illustrate their benefits for distribution-shifted jets, calorimeter showers, and reconstruction-level events.
In all cases, the classifier weights make for a powerful test of the generative network, identify potential problems in the density estimation, relate them to the underlying physics, and tie in with a comprehensive precision and uncertainty treatment for generative networks.

Built on

Event Generation and Statistical Sampling for Physics with Deep Generative Models and a Density Information Buffer ,
Original
S. Otten, S. Caron, W. de Swart, M. van Beekveld, L. Hendriks, C. van Leeuwen, D. Podareanu, R. Ruiz de Austri and R. Verheyen, · 1901
Earlier work this paper cites.
LHC analysis-specific datasets with Generative Adversarial Networks (2019),
Original
B. Hashemi, N. Amin, K. Datta, D. Olivito and M. Pierini, · 1901
Earlier work this paper cites.
ParticleNet: Jet Tagging via Particle Clouds ,
Original
H. Qu and L. Gouskos, · 1902
Earlier work this paper cites.
DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC ,
Original
R. Di Sipio, M. Faucci Giannelli, S. Ketabchi Haghighat and S. Palazzo, · 1903
Earlier work this paper cites.
How to GAN LHC Events ,
Original
A. Butter, T. Plehn and R. Winterhalder, · 1907
Earlier work this paper cites.
OmniFold: A Method to Simultaneously Unfold All Observables ,
Original
A. Andreassen, P. T. Komiske, E. M. Metodiev, B. Nachman and J. Thaler, · 1911
Earlier work this paper cites.
How to GAN Event Subtraction (2019),
Original
A. Butter, T. Plehn and R. Winterhalder, · 1912
Earlier work this paper cites.
Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics ,
Original
D. Belayneh et al. , · 1912
Earlier work this paper cites.
How to GAN away Detector Effects ,
Original
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn and R. Winterhalder, · 1912
Earlier work this paper cites.
Exploring phase space with Neural Importance Sampling ,
Original
E. Bothmann, T. Janßen, M. Knobbe, T. Schmale and S. Schumann, · 2001
Earlier work this paper cites.
i-flow: High-dimensional Integration and Sampling with Normalizing Flows ,
Original
C. Gao, J. Isaacson and C. Krause, · 2001
Earlier work this paper cites.
Event Generation with Normalizing Flows ,
Original
C. Gao, S. Höche, J. Isaacson, C. Krause and H. Schulz, · 2001
Earlier work this paper cites.
Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN) (2020),
Original
Y. Alanazi, N. Sato, T. Liu, W. Melnitchouk, M. P. Kuchera, E. Pritchard, M. Robertson, R. Strauss, L. Velasco and Y. Li, · 2001
Earlier work this paper cites.
Anomaly Detection with Density Estimation ,
Original
B. Nachman and D. Shih, · 2001
Earlier work this paper cites.
On the differences between calorimetric detection of electrons and photons ,
R. Wigmans and M. T. Zeyrek, · 2002
Earlier work this paper cites.
Towards a Computer Vision Particle Flow ,
Original
F. A. Di Bello, S. Ganguly, E. Gross, M. Kado, M. Pitt, L. Santi and J. Shlomi, · 2003
Earlier work this paper cites.
The tradeoff between generative and discriminative classifiers ,
G. Bouchard and B. Triggs, · 2004
Earlier work this paper cites.
Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed ,
Original
E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol and K. Krüger, · 2005
Earlier work this paper cites.
Invertible Networks or Partons to Detector and Back Again ,
Original
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, A. Rousselot, R. Winterhalder, L. Ardizzone and U. Köthe, · 2006
Earlier work this paper cites.
GANplifying event samples ,
Original
A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman and T. Plehn, · 2008
Earlier work this paper cites.
Improved Neural Network Monte Carlo Simulation ,
Original
I.-K. Chen, M. D. Klimek and M. Perelstein, · 2009
Earlier work this paper cites.
Variational Autoencoders for Jet Simulation (2020),
Original
K. Dohi, · 2009
Earlier work this paper cites.
DCTRGAN: Improving the Precision of Generative Models with Reweighting ,
Original
S. Diefenbacher, E. Eren, G. Kasieczka, A. Korol, B. Nachman and D. Shih, · 2009
Earlier work this paper cites.
Mapping Machine-Learned Physics into a Human-Readable Space ,
Original
T. Faucett, J. Thaler and D. Whiteson, · 2010
Earlier work this paper cites.
Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows ,
Original
B. Stienen and R. Verheyen, · 2011
Earlier work this paper cites.
How to GAN Event Unweighting ,
Original
M. Backes, A. Butter, T. Plehn and R. Winterhalder, · 2012
Earlier work this paper cites.
How to GAN Higher Jet Resolution ,
Original
P. Baldi, L. Blecher, A. Butter, J. Collado, J. N. Howard, F. Keilbach, T. Plehn, G. Kasieczka and D. Whiteson, · 2012
Earlier work this paper cites.
Measuring QCD Splittings with Invertible Networks ,
Original
S. Bieringer, A. Butter, T. Heimel, S. Höche, U. Köthe, T. Plehn and S. T. Radev, · 2012
Earlier work this paper cites.
Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis ,
Original
L. de Oliveira, M. Paganini and B. Nachman, · 2017
Earlier work this paper cites.

Similar

Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters ,
Original
M. Paganini, L. de Oliveira and B. Nachman, · 2018
Cited alongside, same era.
Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters ,
Original
L. de Oliveira, M. Paganini and B. Nachman, · 2018
Cited alongside, same era.
CaloGAN : Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks ,
Original
M. Paganini, L. de Oliveira and B. Nachman, · 2018
Cited alongside, same era.
Generating and refining particle detector simulations using the Wasserstein distance in adversarial networks ,
Original
M. Erdmann, L. Geiger, J. Glombitza and D. Schmidt, · 2018
Cited alongside, same era.

Then

Calomplification — the power of generative calorimeter models ,
Original
S. Bieringer, A. Butter, S. Diefenbacher, E. Eren, F. Gaede, D. Hundhausen, G. Kasieczka, B. Nachman, T. Plehn and M. Trabs, · 2022
Later among the works it cites.
An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative training (2022),
Original
M. Backes, A. Butter, M. Dunford and B. Malaescu, · 2022
Later among the works it cites.
Two Invertible Networks for the Matrix Element Method (2022),
Original
A. Butter, T. Heimel, T. Martini, S. Peitzsch and T. Plehn, · 2022
Later among the works it cites.
Classifying anomalies through outer density estimation ,
Original
A. Hallin, J. Isaacson, G. Kasieczka, C. Krause, B. Nachman, T. Quadfasel, M. Schlaffer, D. Shih and M. Sommerhalder, · 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…

Unfolding with Generative Adversarial Networks (2018),

Original

K. Datta, D. Kar and D. Roy, · 2018

Cited alongside, same era.

JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics ,

Original

A. Andreassen, I. Feige, C. Frye and M. D. Schwartz, · 2019

Cited alongside, same era.

Reweighting a parton shower using a neural network: the final-state case ,

Original

E. Bothmann and L. Debbio, · 2019

Cited alongside, same era.

Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network ,

Original

M. Erdmann, J. Glombitza and T. Quast, · 2019

Cited alongside, same era.

Understanding the limitations of conditional generative models ,

Original

E. Fetaya, J.-H. Jacobsen, W. Grathwohl and R. Zemel, · 2019

Cited alongside, same era.

Tech. Rep. ATL-SOFT-PUB-2020-006, CERN, Geneva,

Fast simulation of the ATLAS calorimeter system with Generative Adversarial Networks , · 2020

Cited alongside, same era.

Training normalizing flows with the information bottleneck for competitive generative classification ,

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Cited alongside, same era.

Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network (2021),

Original

E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol and K. Krüger, · 2021

Cited alongside, same era.

Resonant anomaly detection without background sculpting (2022),

Original

A. Hallin, G. Kasieczka, T. Quadfasel, D. Shih and M. Sommerhalder, · 2022

Later among the works it cites.

FETA: Flow-Enhanced Transportation for Anomaly Detection (2022),

Original

T. Golling, S. Klein, R. Mastandrea and B. Nachman, · 2022

Later among the works it cites.

Understanding Event-Generation Networks via Uncertainties ,

Original

M. Bellagente, M. Haussmann, M. Luchmann and T. Plehn, · 2022

Later among the works it cites.

Feature Selection with Distance Correlation (2022),

Original

R. Das, G. Kasieczka and D. Shih, · 2022

Later among the works it cites.

Machine learning and LHC event generation ,

Original

S. Badger et al. , · 2023

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EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets (2023),

Original

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Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning (2023),

Original

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Generative Machine Learning for Detector Response Modeling with a Conditional Normalizing Flow (2023),

Original

A. Xu, S. Han, X. Ju and H. Wang, · 2023

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New Angles on Fast Calorimeter Shower Simulation (2023),

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CURTAINs for your sliding window: Constructing unobserved regions by transforming adjacent intervals ,

Original

J. A. Raine, S. Klein, D. Sengupta and T. Golling, · 2023

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CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimation (2023),

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Fast Point Cloud Generation with Diffusion Models in High Energy Physics (2023),

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Loop Amplitudes from Precision Networks ,

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S. Badger, A. Butter, M. Luchmann, S. Pitz and T. Plehn, · 2023

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One-loop matrix element emulation with factorisation awareness (2023),

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D. Maître and H. Truong, · 2023

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