STI Publications - View Publication Form #16796
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Publication Information
| Title | Artificial Intelligence for reduction and interpretation of subatomic particle production data | ||||
| Abstract | In nuclear and particle physics scattering reactions the most direct observable connecting rates measured in experiments to the underlying probability of the scattering process is the cross section. Cross sections are interpreted in terms of fundamental interactions described by the Standard Model (SM) of nuclear and particle physics. Reactions with more than two particles in the final state are described in a multi-dimensional space in order to fully account for the correlations between the observed particles, which in turn reflect the underlying dynamics. Practical manipulation of the data, however, requires projection to low dimensions, making it difficult to preserve the original correlations between the particle variables. We present a novel approach based on generative adversarial networks (GANs) to encode correlations contained in a multi-dimensional cross section into neural networks (NNs). The NNs trained on event-level experimental data are used as the core of a Monte Carlo e | ||||
| Author(s) | Y. Alanazi Awadh, P. Ambrozewicz, M. Battaglieri, G. Costantini, A. Hiller Blin, E. Isupov, T. Jeske, Y. Li, L. Marsicano, W. Melnitchouk, Victor Mokeev, N. Sato, A. Szczepaniak, T. Viducic | ||||
| Publication Date | January 2021 | ||||
| Document Type | Journal Article | ||||
| Primary Institution | Thomas Jefferson National Accelerator Facility, Newport News | ||||
| Affiliation | Exp Nuclear Physics / Experimental Halls / Hall B | ||||
| Funding Source | Nuclear Physics (NP), STRONG - 2020 - No 824093 | ||||
| Proprietary? | No | ||||
| This publication conveys | Technical Science Results | ||||
| Document Numbers |
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| Associated with an experiment | Yes | ||||
| Experiment Number(s) |
E04-021
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| Associated with EIC | No | ||||
| Supported by Jefferson Lab LDRD Funding | Yes | ||||
| LDRD Number(s) |
2019-LDRD-13
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Journal Article
| Journal Name | Nature |
| Refereed | No |
| Volume | |
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Attachments/Datasets/DOI Link
| Document(s) |
Nature-6.0.docx
(STI Document)
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| Dataset(s) | (none) |
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