Machine Learning for New Physics Searches in B → K∗0µ+µ− Decays

We report the status of a neural network regression model trained to extract new physics (NP) parameters in Monte Carlo (MC) simulation data. We utilize a new EvtGen NP MC generator to generate B → K^∗0µ⁺µ⁻ events according to the deviation of the Wilson Coefficient C<inf>9</inf> from its SM value, δC<inf>9</inf>. We train a three-dimensional ResNet regression model, using images built from the angular observables and the invariant mass of the di-muon system, to extract values of δC<inf>9</inf> directly from the MC data samples. This work is intended for future analyses at the Belle II experiment but may also find applicability at other experiments.