AI for Nuclear Physics: the EXCLAIM project
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909359515107328 |
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| author | Liuti, Simonetta Adams, Douglas Boër, Marie Chern, Gia-Wei Cuic, Marija Engelhardt, Michael Kriesten, Gary R. Goldstein Brandon Li, Yaohang Lin, Huey-Wen Sievert, Matt Sivers, Dennis |
| author_facet | Liuti, Simonetta Adams, Douglas Boër, Marie Chern, Gia-Wei Cuic, Marija Engelhardt, Michael Kriesten, Gary R. Goldstein Brandon Li, Yaohang Lin, Huey-Wen Sievert, Matt Sivers, Dennis |
| contents | In overview of the recent activity of the newly funded EXCLusives with AI and Machine learning (EXCLAIM) collaboration is presented. The main goal of the collaboration is to develop a framework to implement AI and machine learning techniques in problems emerging from the phenomenology of high energy exclusive scattering processes from nucleons and nuclei, maximizing the information that can be extracted from various sets of experimental data, while implementing theoretical constraints from lattice QCD. A specific perspective embraced by EXCLAIM is to use the methods of theoretical physics to understand the working of ML, beyond its standardized applications to physics analyses which most often rely on industrially provided tools, in an automated way. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00163 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI for Nuclear Physics: the EXCLAIM project Liuti, Simonetta Adams, Douglas Boër, Marie Chern, Gia-Wei Cuic, Marija Engelhardt, Michael Kriesten, Gary R. Goldstein Brandon Li, Yaohang Lin, Huey-Wen Sievert, Matt Sivers, Dennis High Energy Physics - Phenomenology In overview of the recent activity of the newly funded EXCLusives with AI and Machine learning (EXCLAIM) collaboration is presented. The main goal of the collaboration is to develop a framework to implement AI and machine learning techniques in problems emerging from the phenomenology of high energy exclusive scattering processes from nucleons and nuclei, maximizing the information that can be extracted from various sets of experimental data, while implementing theoretical constraints from lattice QCD. A specific perspective embraced by EXCLAIM is to use the methods of theoretical physics to understand the working of ML, beyond its standardized applications to physics analyses which most often rely on industrially provided tools, in an automated way. |
| title | AI for Nuclear Physics: the EXCLAIM project |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2408.00163 |