Using Neural Nets for Opposite Side Flavour Tagging

Abstract

This contribution to the proceedings will present different applications of neural networks as a powerful tool to improve the measurement of the B0s oscillation frequency. Opposite side lepton tagger gain performance due to a better particle identification and a better candidate selection. The jet charge tagger uses neural networks to measure the probability of tracks originating from B mesons and improve the identification of jets as b-jet candidates.