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Machine Learning in High Energy Physics Community White Paper
Open accessAbstract
Machine learning is an important applied research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.
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Cites
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
Cited by
Data-driven materials research enabled by natural language processing and information extraction
Olivetti, E. A., Cole, J. M., Kim, E., Kononova, O., Ceder, G., Han, T. Y.-J., & Hiszpanski, A. M. (2020) · Applied Physics Reviews · doi:10.1063/5.0021106 · Cited by 302
Metadata
- Authors
- Albertsson, K., Altoe, P., Anderson, D., Andrews, M., Araque Espinosa, J. P., Aurisano, A., Basara, L., Bevan, A., Bhimji, W., Bonacorsi, D., Calafiura, P., Campanelli, M., Capps, L., Carminati, F., Carrazza, S., Childers, T., Coniavitis, E., Cranmer, K., David, C., Davis, D., Duarte, J., Erdmann, M., Eschle, J., Farbin, A., Feickert, M., Castro, N. F., Fitzpatrick, C., Floris, M., Forti, A., Garra-Tico, J., Gemmler, J., Girone, M., Glaysher, P., Gleyzer, S., Gligorov, V., Golling, T., Graw, J., Gray, L., Greenwood, D., Hacker, T., Harvey, J., Hegner, B., Heinrich, L., Hooberman, B., Junggeburth, J., Kagan, M., Kane, M., Kanishchev, K., Karpiński, P., Kassabov, Z., Kaul, G., Kcira, D., Keck, T., Klimentov, A., Kowalkowski, J., Kreczko, L., Kurepin, A., Kutschke, R., Kuznetsov, V., Köhler, N., Lakomov, I., Lannon, K., Lassnig, M., Limosani, A., Louppe, G., Mangu, A., Mato, P., Meinhard, H., Menasce, D., Moneta, L., Moortgat, S., Narain, M., Neubauer, M., Newman, H., Pabst, H., Paganini, M., Paulini, M., Perdue, G., Perez, U., Picazio, A., Pivarski, J., Prosper, H., Psihas, F., Radovic, A., Reece, R., Rinkevicius, A., Rodrigues, E., Rorie, J., Rousseau, D., Sauers, A., Schramm, S., Schwartzman, A., Severini, H., Seyfert, P., Siroky, F., Skazytkin, K., Sokoloff, M., Stewart, G., Stienen, B., Stockdale, I., Strong, G., Thais, S., Tomko, K., Upfal, E., Usai, E., Ustyuzhanin, A., Vala, M., Vallecorsa, S., Vasel, J., Verzetti, M., Vilasís-Cardona, X., Vlimant, J.-R., Vukotic, I., Wang, S.-J., Watts, G., Williams, M., Wu, W., Wunsch, S., & Zapata, O.
- Venue
- Journal of Physics: Conference Series
- Publisher
- IOP Publishing
- Published
- 2018-09
- Volume
- 1085
- Pages
- 022008
- ISSN
- 1742-6588 (ISSN-L)
- 1742-6596
- OpenAlex
- W2837143805
- Cited by
- 227
- References
- 2
- FWCI
- 76.69
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Albertsson, K., Altoe, P., Anderson, D., Andrews, M., Araque Espinosa, J. P., Aurisano, A., Basara, L., Bevan, A., Bhimji, W., Bonacorsi, D., Calafiura, P., Campanelli, M., Capps, L., Carminati, F., Carrazza, S., Childers, T., Coniavitis, E., Cranmer, K., David, C., . . . Zapata, O. (2018). Machine Learning in High Energy Physics Community White Paper. Journal of Physics: Conference Series. https://doi.org/10.1088/1742-6596/1085/2/022008
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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