Machine Learning for New Physics in B → K*l+l− Decays*

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 → K*l+l− events

according to the deviation of the Wilson Coefficient C9 from its SM value, δC9 .

We train a three-dimensional convolutional neural network regression model,

using images built from the the angular observables and the invariant mass of

the di-lepton system, to extract values of δC9 directly from MC data samples.

This work is intended for future analyses at the Belle II experiment but may

also find applicability at other experiments.