CMS Collaboration; Canelli, Florencia; Kilminster, Benjamin; Caminada, Lea; Botta, Cristina; Aarrestad, Thea; Brzhechko, Danyyl; Cosa, Annapaoloa; Del Burgo, Riccardo; Donato, Silvio; Heikkila, Jaana; Huwiler, Marc; Jofrehei, Arash; Leontsinis, Stefanos; Macchiolo, Anna; Meiring, Peter; Mikuni, Vinicius; Missiroli, Marino; Molinatti, Umberto; Neutelings, Izaak; Rauco, Giorgia; Robmann, Peter; Salerno, Daniel; Schweiger, Korbinian; Takahashi, Yuta; Wertz, Sebastien; Zucchetta, Alberto; et al (2020). Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques. Journal of Analytical Sciences, Methods and Instrumentation, 15(06):P06005.
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| Item Type: | Journal Article, refereed, original work |
|---|---|
| Communities & Collections: | 07 Faculty of Science > Physics Institute |
| Dewey Decimal Classification: | 530 Physics |
| Scopus Subject Areas: | Physical Sciences > Mathematical Physics
Physical Sciences > Instrumentation |
| Language: | English |
| Date: | 2020 |
| Deposited On: | 08 Feb 2021 10:49 |
| Last Modified: | 27 Jan 2022 04:34 |
| Publisher: | Scientific Research Publishing, Inc. |
| ISSN: | 2164-2745 |
| OA Status: | Hybrid |
| Free access at: | Publisher DOI. An embargo period may apply. |
| Publisher DOI: | https://doi.org/10.1088/1748-0221/15/06/P06005 |
CMS Collaboration