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In this paper, we present a new method to efficiently generate jets in High Energy Physics called PC-JeDi.
B. Hashemi et al. , · 1901
Earlier work this paper cites.
Generative modeling by estimating gradients of the data distribution ,
Y. Song and S. Ermon, · 1907
Earlier work this paper cites.
Reverse-time diffusion equation models ,
B. D. Anderson, · 1982
Earlier work this paper cites.
Numerical Solution of Stochastic Differential Equations ,
P. E. Kloeden and E. Platen, · 1992
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching ,
A. Hyvärinen, · 2005
Earlier work this paper cites.
Denoising diffusion probabilistic models ,
J. Ho, A. Jain and P. Abbeel, · 2006
Earlier work this paper cites.
Improved techniques for training score-based generative models ,
Y. Song and S. Ermon, · 2006
Earlier work this paper cites.
Learning to be bayesian without supervision ,
M. Raphan and E. Simoncelli, · 2006
Earlier work this paper cites.
The ATLAS Experiment at the CERN Large Hadron Collider ,
ATLAS Collaboration, · 2008
Earlier work this paper cites.
The CMS experiment at the CERN LHC ,
CMS Collaboration, · 2008
Earlier work this paper cites.
Generative Networks for LHC events (2020),
A. Butter and T. Plehn, · 2008
Earlier work this paper cites.
The anti-kt jet clustering algorithm ,
M. Cacciari, G. P. Salam and G. Soyez, · 2008
Earlier work this paper cites.
Denoising diffusion implicit models (2020), https://arxiv.org/abs/2010.02502
J. Song, C. Meng and S. Ermon, · 2010
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations ,
Y. Song et al. , · 2011
Earlier work this paper cites.
Least squares estimation without priors or supervision ,
M. Raphan and E. P. Simoncelli., · 2011
Earlier work this paper cites.
A connection between score matching and denoising autoencoders ,
P. Vincent, · 2011
Earlier work this paper cites.
Identifying boosted objects with n-subjettiness ,
J. Thaler and K. V. Tilburg, · 2011
Earlier work this paper cites.
Equivariant Energy Flow Networks for Jet Tagging ,
M. J. Dolan and A. Ore, · 2012
Earlier work this paper cites.
Energy correlation functions for jet substructure ,
A. J. Larkoski, G. P. Salam and J. Thaler, · 2013
Earlier work this paper cites.
The Fast Simulation of the CMS Experiment ,
A. Giammanco, · 2014
Earlier work this paper cites.
Power counting to better jet observables ,
A. J. Larkoski, I. Moult and D. Neill, · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization ,
D. P. Kingma and J. Ba, · 2015
Earlier work this paper cites.
Jet-images — deep learning edition ,
L. de Oliveira et al. , · 2016
Earlier work this paper cites.
Modeling and Analysis of Modern Fluid Problems ,
L. Zheng and X. Zhang, · 2017
Earlier work this paper cites.
Particle-flow reconstruction and global event description with the CMS detector ,
The CMS Collaboration, · 2017
Earlier work this paper cites.
J. Pearkes et al. , · 2017
Earlier work this paper cites.
Deep-learning top taggers or the end of QCD? ,
G. Kasieczka et al. , · 2017
Earlier work this paper cites.
M. Zaheer et al. , · 2017
Earlier work this paper cites.
A. Vaswani et al. , · 2017
Earlier work this paper cites.
ReDecay: A novel approach to speed up the simulation at LHCb ,
D. Müller et al. , · 2018
Earlier work this paper cites.
The new Fast Calorimeter Simulation in ATLAS ,
The ATLAS Collaboration, · 2018
Cited alongside, same era.
Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters ,
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 ,
M. Paganini, L. de Oliveira and B. Nachman, · 2018
Cited alongside, same era.
Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters ,
M. Paganini, L. de Oliveira and B. Nachman, · 2018
Cited alongside, same era.
Energy flow polynomials: a complete linear basis for jet substructure ,
P. T. Komiske, E. M. Metodiev and J. Thaler, · 2018
Cited alongside, same era.
Particle cloud generation with message passing generative adversarial networks ,
R. Kansal et al. , · 2021
Later among the works it cites.
Graph Generative Models for Fast Detector Simulations in High Energy Physics (2021),
A. Hariri, D. Dyachkova and S. Gleyzer, · 2021
Later among the works it cites.
Diffusion models beat gans on image synthesis ,
P. Dhariwal and A. Nichol, · 2021
Later among the works it cites.
Gotta go fast when generating data with score-based models (2021), https://arxiv.org/abs/2105.14080
A. Jolicoeur-Martineau et al. , · 2021
Later among the works it cites.
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A. Butter et al. , · 2018
Cited alongside, same era.
Pulling out all the tops with computer vision and deep learning ,
S. Macaluso and D. Shih, · 2018
Cited alongside, same era.
P. Battaglia et al. , · 2018
Cited alongside, same era.
The Machine Learning landscape of top taggers ,
G. Kasieczka et al. , · 2019
Cited alongside, same era.
Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network ,
M. Erdmann, J. Glombitza and T. Quast, · 2019
Cited alongside, same era.
DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC ,
R. Di Sipio et al. , · 2019
Cited alongside, same era.
How to GAN LHC Events ,
A. Butter, T. Plehn and R. Winterhalder, · 2019
Cited alongside, same era.
A. Q. Nichol and P. Dhariwal, · 2021
Later among the works it cites.
Jet tagging in the Lund plane with graph networks ,
F. A. Dreyer and H. Qu, · 2021
Later among the works it cites.
Point cloud transformers applied to collider physics ,
V. Mikuni and F. Canelli, · 2021
Later among the works it cites.
Particle Convolution for High Energy Physics (2021), 2107.02908
C. Shimmin, · 2021
Later among the works it cites.
S. Shleifer, J. Weston and M. Ott, · 2021
Later among the works it cites.
The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider ,
T. Aarrestad et al. , · 2022
Later among the works it cites.
AtlFast3: the next generation of fast simulation in ATLAS ,
The ATLAS Collaboration, · 2022
Later among the works it cites.
The ATLAS Collaboration, · 2022
Later among the works it cites.
A. Adelmann et al. , · 2022
Later among the works it cites.
Learning to simulate high energy particle collisions from unlabeled data ,
J. N. Howard et al. , · 2022
Later among the works it cites.
JetFlow: Generating Jets with Conditioned and Mass Constrained Normalising Flows (2022),
B. Käch, D. Krücker, I. Melzer-Pellmann, M. Scham, S. Schnake and A. Verney-Provatas, · 2022
Later among the works it cites.
Point Cloud Generation using Transformer Encoders and Normalising Flows (2022),
B. Käch, D. Krücker and I. Melzer-Pellmann, · 2022
Later among the works it cites.
R. Kansal et al. , · 2022
Later among the works it cites.
Elucidating the design space of diffusion-based generative models ,
T. Karras et al. , · 2022
Later among the works it cites.
A. Ramesh et al. , · 2022
Later among the works it cites.
Score-based generative models for calorimeter shower simulation ,
V. Mikuni and B. Nachman, · 2022
Later among the works it cites.
Equivariant diffusion for molecule generation in 3d ,
E. Hoogeboom et al. , · 2022
Later among the works it cites.
B. L. Trippe et al. , · 2022
Later among the works it cites.
C. Lu et al. , · 2022
Later among the works it cites.
An efficient Lorentz equivariant graph neural network for jet tagging ,
S. Gong et al. , · 2022
Later among the works it cites.
Particle Transformer for Jet Tagging (2022),
H. Qu, C. Li and S. Qian, · 2022
Later among the works it cites.
Jetnet ,
R. Kansal et al. , · 2022
Later among the works it cites.
Classifier-free diffusion guidance (2022), https://arxiv.org/abs/2207.12598
J. Ho and T. Salimans, · 2022
Later among the works it cites.
EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets (2023),
E. Buhmann, G. Kasieczka and J. Thaler, · 2023
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How to Understand Limitations of Generative Networks (2023),
R. Das, L. Favaro, T. Heimel, C. Krause, T. Plehn and D. Shih, · 2023
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