Effectiveness of denoising diffusion probabilistic models for fast and high-fidelity whole-event simulation in high-energy heavy-ion experiments

Y. Song and S. Ermon, Generative modeling by estimating gradients of the data distribution, CoRR abs/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.

X.-N. Wang and M. Gyulassy, HIJING: A Monte Carlo model for multiple jet production in p p, p A and A A collisions, Phys. Rev. D 44

1991

Earlier work this paper cites.

S. Agostinelli et al. (GEANT4), GEANT4–a simulation toolkit, Nucl. Instrum. Meth. A 506

2003

Earlier work this paper cites.

J. Allison et al. , Geant4 developments and applications, IEEE Trans. Nucl. Sci. 53

2006

Earlier work this paper cites.

G. E. Hinton and R. R. Salakhutdinov, Reducing the dimensionality of data with neural networks, science 313

2006

Earlier work this paper cites.

J. Song, C. Meng, and S. Ermon, Denoising diffusion implicit models, arXiv preprint arXiv:2010.02502 (2020a)

Original

2010

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, arXiv preprint arXiv:2011.13456 (2020b)

Original

2011

Earlier work this paper cites.

R. Kansal, J. Duarte, B. Orzari, T. Tomei, M. Pierini, M. Touranakou, J.-R. Vlimant, and D. Gunopulos, Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics, in 34th Conference on Neural Information Processing Systems (2020) arXiv:2012.00173 [physics.data-an]

Original

2012

Earlier work this paper cites.

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, Generative adversarial nets, Advances in neural information processing systems 27

2014

Earlier work this paper cites.

D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980 (2014)

Original

2014

Earlier work this paper cites.

D. Rezende and S. Mohamed, Variational inference with normalizing flows, in International conference on machine learning (PMLR, 2015) pp. 1530–1538

2015

Earlier work this paper cites.

J. Sohl-Dickstein, E. A. Weiss, N. Maheswaranathan, and S. Ganguli, Deep Unsupervised Learning using Nonequilibrium Thermodynamics, (2015), arXiv:1503.03585 [cs.LG]

Original

2015

Earlier work this paper cites.

A. Adare et al. (PHENIX), An Upgrade Proposal from the PHENIX Collaboration, (2015), arXiv:1501.06197 [nucl-ex]

Original

2015

Earlier work this paper cites.

A. Radford, L. Metz, and S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial networks, arXiv preprint arXiv:1511.06434 (2015)

Original

2015

Earlier work this paper cites.

O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutional networks for biomedical image segmentation, in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18 (Springer, 2015) pp. 234–241

2015

Cited alongside, same era.

J. Allison et al. , Recent developments in Geant4, Nucl. Instrum. Meth. A 835

2016

Cited alongside, same era.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE conference on computer vision and pattern recognition (2016) pp. 770–778

2016

Cited alongside, same era.

L. de Oliveira, M. Paganini, and B. Nachman, Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis, Comput. Softw. Big Sci. 1

Original

2017

Cited alongside, same era.

G. R. Khattak, S. Vallecorsa, F. Carminati, and G. M. Khan, High Energy Physics Calorimeter Detector Simulation Using Generative Adversarial Networks With Domain Related Constraints, IEEE Access 9

2021

Later among the works it cites.

A. Q. Nichol and P. Dhariwal, Improved denoising diffusion probabilistic models, in International Conference on Machine Learning (PMLR, 2021) pp. 8162–8171

2021

Later among the works it cites.

P. Shanahan et al. , Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning, (2022), arXiv:2209.07559 [physics.comp-ph]

Original

2022

Later among the works it cites.

A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen, Hierarchical text-conditional image generation with clip latents, arXiv preprint arXiv:2204.06125 1

Original

2022

Later among the works it cites.