Machine learning at the energy and intensity frontiers of particle physics
- DOI
- 10.1038/s41586-018-0361-2
- Published
- 2018-08
- Container
- Nature
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.1038/s41586-018-0361-2,
title = {Machine learning at the energy and intensity frontiers of particle physics},
author = {Alexander Radovic and Mike Williams and David Rousseau and Michael Kagan and Daniele Bonacorsi and Alexander Himmel and Adam Aurisano and Kazuhiro Terao and Taritree Wongjirad},
year = {2018},
journal = {Nature},
doi = {10.1038/s41586-018-0361-2},
url = {https://doi.org/10.1038/s41586-018-0361-2}
}RIS
TY - JOUR TI - Machine learning at the energy and intensity frontiers of particle physics AU - Alexander Radovic AU - Mike Williams AU - David Rousseau AU - Michael Kagan AU - Daniele Bonacorsi AU - Alexander Himmel AU - Adam Aurisano AU - Kazuhiro Terao AU - Taritree Wongjirad PY - 2018 JO - Nature DO - 10.1038/s41586-018-0361-2 UR - https://doi.org/10.1038/s41586-018-0361-2 ER -
APA
Radovic, A., Williams, M., Rousseau, D., Kagan, M., Bonacorsi, D., Himmel, A., Aurisano, A., Terao, K., & Wongjirad, T. (2018). Machine learning at the energy and intensity frontiers of particle physics. Nature. https://doi.org/10.1038/s41586-018-0361-2
Source records
- crossref · retrieved 2026-11-08T00:08:59.309Z