Mar 16 – 17, 2021
Remote only
Europe/Paris timezone

Reconstruction of di-tau mass using deep neural networks

Mar 16, 2021, 9:45 AM
15m
Remote only

Remote only

Speaker

Lucas TORTEROTOT ({UNIV CLAUDE BERNARD}UMR5822)

Description

Reconstruction of di-$\tau$ mass in a faster and more accurate way than the existing methods is crucial to test any theory involving Higgs boson and Z boson which are decaying to $\tau^+ \tau^-$. However, it is an arduous task due to existence of neutrinos as decay product of each $\tau$ lepton which are invisible to detectors at LHC.

The present ongoing work aims at obtaining a di-$\tau$ mass estimator using ML techniques. Its use in the CMS MSSM $H\to\tau\tau$ analysis on the full Run II will be discussed.

Author

Lucas TORTEROTOT ({UNIV CLAUDE BERNARD}UMR5822)

Co-authors

Ece Asilar (IPNL) Colin Bernet (IPNL/IN2P3)

Presentation materials