The DOI matches its Crossref record; 249 of the 249 references it cites were found in a registry or an index.
Colloquium : Machine learning in nuclear physics
Open accessAbstract
Nuclear physics deals with complex systems, large datasets, and complicated correlations between parameters, which makes the field suitable for the application of machine learning techniques. Machine learning can help classify and analyze data, find hidden correlations, and assist in the design of new experiments and detectors. This Colloquium explains how this will lead to advances in nuclear theory, experimental methods and data acquisition, and accelerator technology.
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Cites
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Metadata
- Authors
- Boehnlein, A., Diefenthaler, M., Sato, N., Schram, M., Ziegler, V., Fanelli, C., Hjorth-Jensen, M., Horn, T., Kuchera, M. P., Lee, D., Nazarewicz, W., Ostroumov, P., Orginos, K., Poon, A., Wang, X.-N., Scheinker, A., Smith, M. S., & Pang, L.-G.
- Venue
- Reviews of Modern Physics
- Publisher
- American Physical Society (APS)
- Published
- 2022-09-08
- Volume
- 94
- Issue
- 3
- ISSN
- 0034-6861 (ISSN-L)
- 1539-0756
- OpenAlex
- W4295101746
- Cited by
- 257
- References
- 249
- FWCI
- 6.15
- Type
- journal article
- Language
- en
- Open access
- green
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- Boehnlein, A., Diefenthaler, M., Sato, N., Schram, M., Ziegler, V., Fanelli, C., Hjorth-Jensen, M., Horn, T., Kuchera, M. P., Lee, D., Nazarewicz, W., Ostroumov, P., Orginos, K., Poon, A., Wang, X.-N., Scheinker, A., Smith, M. S., & Pang, L.-G. (2022). Colloquium : Machine learning in nuclear physics. Reviews of Modern Physics. https://doi.org/10.1103/revmodphys.94.031003
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
