Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
| dc.citation.articleNumber | P06005 | |
| dc.citation.journalTitle | Journal of Instrumentation | |
| dc.citation.volumeNumber | 15 | |
| dc.contributor.author | The CMS collaboration | |
| dc.date.accessioned | 2020-11-04T19:55:49Z | |
| dc.date.available | 2020-11-04T19:55:49Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The identification performances of a variety of algorithms are characterized in simulated events and directly compared with data. The algorithms are validated using proton-proton collision data at √s = 13TeV, corresponding to an integrated luminosity of 35.9 fb−1. Systematic uncertainties are assessed by comparing the results obtained using simulation and collision data. The new techniques studied in this paper provide significant performance improvements over non-ML techniques, reducing the background rate by up to an order of magnitude at the same signal efficiency. | |
| dc.identifier.citation | The CMS collaboration. "Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques." <i>Journal of Instrumentation,</i> 15, (2020) IOP: https://doi.org/10.1088/1748-0221/15/06/P06005. | |
| dc.identifier.doi | https://doi.org/10.1088/1748-0221/15/06/P06005 | |
| dc.identifier.uri | https://hdl.handle.net/1911/109513 | |
| dc.language.iso | eng | |
| dc.publisher | IOP | |
| dc.rights | Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence . Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. | |
| dc.rights.uri | https://creativecommons.org/licenses/by/3.0/ | |
| dc.title | Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques | |
| dc.type | Journal article | |
| dc.type.dcmi | Text | |
| dc.type.publication | publisher version |
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