Identification of hadronic tau lepton decays using a deep neural network
| dc.citation.articleNumber | P07023 | |
| dc.citation.issueNumber | 7 | |
| dc.citation.journalTitle | Journal of Instrumentation | |
| dc.citation.volumeNumber | 17 | |
| dc.contributor.author | The CMS collaboration | |
| dc.date.accessioned | 2022-09-01T14:18:20Z | |
| dc.date.available | 2022-09-01T14:18:20Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (τh) that originate from genuine tau leptons in the CMS detector against τh candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a τh candidate and employs a deep neural network with convolutional layers to efficiently process the inputs. This algorithm leads to a significantly improved performance compared with the previously used one. For example, the efficiency for a genuine τh to pass the discriminator against jets increases by 10–30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient τh reconstruction is introduced that incorporates additional hadronic decay modes. The superior performance of the new algorithm to discriminate against jets, electrons, and muons and the improved τh reconstruction method are validated with LHC proton-proton collision data at √s = 13 TeV. | |
| dc.identifier.citation | The CMS collaboration. "Identification of hadronic tau lepton decays using a deep neural network." <i>Journal of Instrumentation,</i> 17, no. 7 (2022) IOP Publishing: https://doi.org/10.1088/1748-0221/17/07/P07023. | |
| dc.identifier.digital | Tumasyan_2022 | |
| dc.identifier.doi | https://doi.org/10.1088/1748-0221/17/07/P07023 | |
| dc.identifier.uri | https://hdl.handle.net/1911/113164 | |
| dc.language.iso | eng | |
| dc.publisher | IOP Publishing | |
| dc.rights | Published by IOP Publishing Ltd on behalf of Sissa Medialab. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 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/4.0/ | |
| dc.title | Identification of hadronic tau lepton decays using a deep neural network | |
| dc.type | Journal article | |
| dc.type.dcmi | Text | |
| dc.type.publication | publisher version |
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