Speaker
Description
The CMS experiment has deployed for the Run 2 LHC data-taking period a Convolutional Neural Network architecture to identify hadronically decaying tau leptons against quark and gluon jets, electrons, and muons: the DeepTau algorithm. For the LHC Run 3, this algorithm saw an important upgrade with the introduction of domain adaptation techniques in order to improve its performance and achieve better modeling of the behavior in simulation with respect to recorded data. Further improvements to the network architectures are also discussed, together with its performance in early Run 3 data. This talk also provides an overview of other algorithms used within the CMS experiment for the identification of hadronically decaying taus in standard or rare topologies.
| Would you like to be considered for an oral presentation? | Yes |
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