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We introduce two diffusion models and an autoregressive transformer for LHC physics simulations.
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),
B. Hashemi, N. Amin, K. Datta, D. Olivito and M. Pierini, · 1901
Earlier work this paper cites.
R. Di Sipio, M. Faucci Giannelli, S. Ketabchi Haghighat and S. Palazzo, · 1903
Earlier work this paper cites.
Deep-Learning Jets with Uncertainties and More ,
S. Bollweg, M. Haußmann, G. Kasieczka, M. Luchmann, T. Plehn and J. Thompson, · 1904
Earlier work this paper cites.
Event Generation with Sherpa 2.2 ,
E. Bothmann et al. , · 1905
Earlier work this paper cites.
A. Butter, T. Plehn and R. Winterhalder, · 1907
Earlier work this paper cites.
Normalizing flows: An introduction and review of current methods (2019),
I. Kobyzev, S. Prince and M. A. Brubaker, · 1908
Earlier work this paper cites.
OmniFold: A Method to Simultaneously Unfold All Observables ,
A. Andreassen, P. T. Komiske, E. M. Metodiev, B. Nachman and J. Thaler, · 1911
Earlier work this paper cites.
(Machine) Learning Amplitudes for Faster Event Generation (2019),
F. Bishara and M. Montull, · 1912
Earlier work this paper cites.
How to GAN Event Subtraction (2019),
A. Butter, T. Plehn and R. Winterhalder, · 1912
Earlier work this paper cites.
Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics ,
D. Belayneh et al. , · 1912
Earlier work this paper cites.
Normalizing flows for probabilistic modeling and inference (2019),
G. Papamakarios, E. Nalisnick, D. J. Rezende, S. Mohamed and B. Lakshminarayanan, · 1912
Earlier work this paper cites.
How to GAN away Detector Effects ,
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn and R. Winterhalder, · 1912
Earlier work this paper cites.
Probable Networks and Plausible Predictions – A Review of Practical Bayesian Methods for Supervised Neural Networks ,
D. MacKay, · 1995
Earlier work this paper cites.
Bayesian learning for neural networks ,
R. M. Neal, · 1995
Earlier work this paper cites.
Exploring phase space with Neural Importance Sampling ,
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 ,
C. Gao, J. Isaacson and C. Krause, · 2001
Earlier work this paper cites.
Event Generation with Normalizing Flows ,
C. Gao, S. Höche, J. Isaacson, C. Krause and H. Schulz, · 2001
Earlier work this paper cites.
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.
QCD matrix elements + parton showers ,
S. Catani, F. Krauss, R. Kuhn and B. R. Webber, · 2001
Earlier work this paper cites.
Using neural networks for efficient evaluation of high multiplicity scattering amplitudes ,
S. Badger and J. Bullock, · 2002
Earlier work this paper cites.
Towards a Computer Vision Particle Flow ,
F. A. Di Bello, S. Ganguly, E. Gross, M. Kado, M. Pitt, L. Santi and J. Shlomi, · 2003
Earlier work this paper cites.
Per-Object Systematics using Deep-Learned Calibration ,
G. Kasieczka, M. Luchmann, F. Otterpohl and T. Plehn, · 2003
Earlier work this paper cites.
Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed ,
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 ,
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, A. Rousselot, R. Winterhalder, L. Ardizzone and U. Köthe, · 2006
Earlier work this paper cites.
Generative Networks for LHC events (2020),
A. Butter and T. Plehn, · 2008
Earlier work this paper cites.
A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman and T. Plehn, · 2008
Earlier work this paper cites.
The anti- k t k_{t} jet clustering algorithm ,
M. Cacciari, G. P. Salam and G. Soyez, · 2008
Earlier work this paper cites.
Improved Neural Network Monte Carlo Simulation ,
I.-K. Chen, M. D. Klimek and M. Perelstein, · 2009
Earlier work this paper cites.
Variational Autoencoders for Jet Simulation (2020),
K. Dohi, · 2009
Earlier work this paper cites.
DCTRGAN: Improving the Precision of Generative Models with Reweighting ,
S. Diefenbacher, E. Eren, G. Kasieczka, A. Korol, B. Nachman and D. Shih, · 2009
Earlier work this paper cites.
Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows ,
B. Stienen and R. Verheyen, · 2011
Cited alongside, same era.
How to GAN Event Unweighting ,
M. Backes, A. Butter, T. Plehn and R. Winterhalder, · 2012
Cited alongside, same era.
How to GAN Higher Jet Resolution ,
P. Baldi, L. Blecher, A. Butter, J. Collado, J. N. Howard, F. Keilbach, T. Plehn, G. Kasieczka and D. Whiteson, · 2012
Cited alongside, same era.
Measuring QCD Splittings with Invertible Networks ,
S. Bieringer, A. Butter, T. Heimel, S. Höche, U. Köthe, T. Plehn and S. T. Radev, · 2012
Cited alongside, same era.
Generative Networks for Precision Enthusiasts (2021),
A. Butter, T. Heimel, S. Hummerich, T. Krebs, T. Plehn, A. Rousselot and S. Vent, · 2021
Later among the works it cites.
Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network (2021),
E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol and K. Krüger, · 2021
Later among the works it cites.
Analysis-Specific Fast Simulation at the LHC with Deep Learning ,
C. Chen, O. Cerri, T. Q. Nguyen, J. R. Vlimant and M. Pierini, · 2021
Later among the works it cites.
CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows (2021),
C. Krause and D. Shih, · 2021
Later among the works it cites.
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M. Cacciari, G. P. Salam and G. Soyez, · 2012
Cited alongside, same era.
Variational inference with normalizing flows ,
D. Rezende and S. Mohamed, · 2015
Cited alongside, same era.
Deep unsupervised learning using nonequilibrium thermodynamics ,
J. Sohl-Dickstein, E. A. Weiss, N. Maheswaranathan and S. Ganguli, · 2015
Cited alongside, same era.
Density estimation using real nvp (2016),
L. Dinh, J. Sohl-Dickstein and S. Bengio, · 2016
Cited alongside, same era.
Uncertainty in Deep Learning ,
Y. Gal, · 2016
Cited alongside, same era.
L. de Oliveira, M. Paganini and B. Nachman, · 2017
Cited alongside, same era.
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? ,
A. Kendall and Y. Gal, · 2017
Cited alongside, same era.
Variational inference: A review for statisticians ,
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Cited alongside, same era.
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A factorisation-aware Matrix element emulator ,
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Variational diffusion models ,
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Machine Learning and LHC Event Generation (2022),
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MadNIS – Neural Multi-Channel Importance Sampling (2022),
T. Heimel, R. Winterhalder, A. Butter, J. Isaacson, C. Krause, F. Maltoni, O. Mattelaer and T. Plehn, · 2022
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CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds ,
J. C. Cresswell, B. L. Ross, G. Loaiza-Ganem, H. Reyes-Gonzalez, M. Letizia and A. L. Caterini, · 2022
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Score-based generative models for calorimeter shower simulation ,
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Calomplification — the power of generative calorimeter models ,
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M. Backes, A. Butter, M. Dunford and B. Malaescu, · 2022
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Two Invertible Networks for the Matrix Element Method (2022),
A. Butter, T. Heimel, T. Martini, S. Peitzsch and T. Plehn, · 2022
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Understanding Event-Generation Networks via Uncertainties ,
M. Bellagente, M. Haussmann, M. Luchmann and T. Plehn, · 2022
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X. Liu, C. Gong and Q. Liu, · 2022
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Loop Amplitudes from Precision Networks (2022),
S. Badger, A. Butter, M. Luchmann, S. Pitz and T. Plehn, · 2022
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Geometry-aware Autoregressive Models for Calorimeter Shower Simulations ,
J. Liu, A. Ghosh, D. Smith, P. Baldi and D. Whiteson, · 2022
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Modern Machine Learning for LHC Physicists (2022),
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EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets (2023),
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L2LFlows: Generating High-Fidelity 3D Calorimeter Images (2023),
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New Angles on Fast Calorimeter Shower Simulation (2023),
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Fast Point Cloud Generation with Diffusion Models in High Energy Physics (2023),
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