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.
Original language | English (US) |
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Article number | P07023 |
Journal | Journal of Instrumentation |
Volume | 17 |
Issue number | 7 |
DOIs | |
State | Published - Jul 1 2022 |
All Science Journal Classification (ASJC) codes
- Instrumentation
- Mathematical Physics
Keywords
- Large detector systems for particle and astroparticle physics
- Particle identification methods
- Pattern recognition
- calibration and fitting methods
- cluster finding