Identification of hadronic tau lepton decays using a deep neural network
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Type :
Article
Publication Status :
Published
Access :
openAccess
https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/
Abstract
A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (τ h) that originate from genuine tau leptons in the CMS detector against τ h candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a τ h 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 τ h to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient τ h 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 τ h reconstruction method are validated with LHC proton-proton collision data at s = 13 TeV.
Source :
Journal of Instrumentation
Date :
2022-07
Volume :
17
Issue :
7
Publisher :
IOP Publishing
URI
http://hdl.handle.net/10679/8439https://iopscience.iop.org/article/10.1088/1748-0221/17/07/P07023
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