Electron-nucleus cross sections from transfer learning
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916900425957376 |
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| author | Graczyk, Krzysztof M. Kowal, Beata E. Ankowski, Artur M. Banerjee, Rwik Dharmapal Bonilla, Jose Luis Prasad, Hemant Sobczyk, Jan T. |
| author_facet | Graczyk, Krzysztof M. Kowal, Beata E. Ankowski, Artur M. Banerjee, Rwik Dharmapal Bonilla, Jose Luis Prasad, Hemant Sobczyk, Jan T. |
| contents | Transfer learning (TL) allows a deep neural network (DNN) trained on one type of data to be adapted for new problems with limited information. We propose to use the TL technique in physics. The DNN learns the details of one process, and after fine-tuning, it makes predictions for related processes. We consider the DNNs, trained on inclusive electron-carbon scattering data, and show that after fine-tuning, they accurately predict cross sections for electron interactions with nuclear targets ranging from helium-3 to iron. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_09936 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Electron-nucleus cross sections from transfer learning Graczyk, Krzysztof M. Kowal, Beata E. Ankowski, Artur M. Banerjee, Rwik Dharmapal Bonilla, Jose Luis Prasad, Hemant Sobczyk, Jan T. High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment Nuclear Experiment Nuclear Theory Transfer learning (TL) allows a deep neural network (DNN) trained on one type of data to be adapted for new problems with limited information. We propose to use the TL technique in physics. The DNN learns the details of one process, and after fine-tuning, it makes predictions for related processes. We consider the DNNs, trained on inclusive electron-carbon scattering data, and show that after fine-tuning, they accurately predict cross sections for electron interactions with nuclear targets ranging from helium-3 to iron. |
| title | Electron-nucleus cross sections from transfer learning |
| topic | High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment Nuclear Experiment Nuclear Theory |
| url | https://arxiv.org/abs/2408.09936 |