The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
Open accessAbstract
We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in high energy particle physics by applying a novel Generative Adversarial Network (GAN) architecture to the production of jet images—2D representations of energy depositions from particles interacting with a calorimeter. We propose a simple architecture, the Location-Aware Generative Adversarial Network, that learns to produce realistic radiation patterns from simulated high energy particle collisions. The pixel intensities of GAN-generated images faithfully span over many orders of magnitude and exhibit the desired low-dimensional physical properties (i.e., jet mass, n-subjettiness, etc.). We shed light on limitations, and provide a novel empirical validation of image quality and validity of GAN-produced simulations of the natural world. This work provides a base for further explorations of GANs for use in faster simulation in high energy particle physics.
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235
Sjöstrand, T., Mrenna, S., & Skands, P. (2006) · Journal of High Energy Physics · doi:10.1088/1126-6708/2006/05/026 · Cited by 8,952
Conditional Generative Adversarial Nets
Mirza, M., & Osindero, S. (2014) · arXiv · doi:10.48550/arxiv.1411.1784 · Cited by 8,863
Improved Techniques for Training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016) · arXiv · doi:10.48550/arxiv.1606.03498 · Cited by 6,965
A brief introduction to PYTHIA 8.1
Sjöstrand, T., Mrenna, S., & Skands, P. (2008) · Computer Physics Communications · doi:10.1016/j.cpc.2008.01.036 · Cited by 5,435
The Earth Mover's Distance as a Metric for Image Retrieval
Rubner, Y., Tomasi, C., & Guibas, L. J. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026543900054 · Cited by 4,568
StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks
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The ATLAS Simulation Infrastructure
The ATLAS Collaboration, Aad, G., Abbott, B., Abdallah, J., Abdelalim, A. A., Abdesselam, A., Abdinov, O., Abi, B., Abolins, M., Abramowicz, H., et al. (2010) · The European Physical Journal C · doi:10.1140/epjc/s10052-010-1429-9 · Cited by 1,675
Jet-images — deep learning edition
de Oliveira, L., Kagan, M., Mackey, L., Nachman, B., & Schwartzman, A. (2016) · Journal of High Energy Physics · doi:10.1007/jhep07(2016)069 · Cited by 282
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Generative adversarial networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020) · Communications of the ACM · doi:10.1145/3422622 · Cited by 18,384
Generative adversarial network: An overview of theory and applications
Aggarwal, A., Mittal, M., & Battineni, G. (2021) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2020.100004 · Cited by 783
Zhu, Y., & Zabaras, N. (2018) · Journal of Computational Physics · doi:10.1016/j.jcp.2018.04.018 · Cited by 704
Quantum generative adversarial networks
Dallaire-Demers, P.-L., & Killoran, N. (2018) · Physical Review A · doi:10.1103/physreva.98.012324 · Cited by 506
Deep learning and its application to LHC physics
Guest, D., Cranmer, K., & Whiteson, D. (2018) · Annual Review of Nuclear and Particle Science · doi:10.1146/annurev-nucl-101917-021019 · Cited by 417
Metadata
- Authors
- de Oliveira, L., Paganini, M., & Nachman, B.
- Venue
- Computing and Software for Big Science
- Published
- 2017-09-29
- Volume
- 1
- Issue
- 1
- ISSN
- 2510-2036 (ISSN-L)
- 2510-2044
- OpenAlex
- W2581875816
- Cited by
- 293
- References
- 13
- FWCI
- 17.04
- Type
- journal article
- Language
- en
- Open access
- green
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- de Oliveira, L., Paganini, M., & Nachman, B. (2017). Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis. Computing and Software for Big Science. https://doi.org/10.1007/s41781-017-0004-6
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