Title: Machine Learning for New Physics in B→ K*0l+l- Decays

Speaker: Dr. Shawn Dubey, University of Hawaii

Abstract:

In this work, we report the status of a neural network regression model trained to extract new physics (NP) parameters in Monte Carlo (MC) data.  We utilize a new EvtGen NP MC generator to generate $B \rightarrow K^{*} \mu^{+} \mu^{-}$ events according to certain NP parameterizations.  We train a three-dimensional convolutional neural network (CNN) regression model, using images built from the angular observables and the invariant mass of the di-muon system, to extract information directly from the data.  This work is intended for future analyses at the Belle II experiment but may also find applicability at other experiments.