Machine Learning for New Physics Searches in B0 → K∗0μ+μ− Decays

We report on a novel application of computer vision techniques to extract beyond the Standard Model (BSM) parameters directly from high energy physics (HEP) flavor data. We develop a method of transforming angular and kinematic distributions into “quasi-images" that can be used to train a convolutional neural network to perform regression tasks, similar to fitting. This contrasts with the usual classification functions performed using ML/AI in HEP. As a proof-of-concept, we train a 34-layer Residual Neural Network (ResNet) to regress on these images and determine the Wilson Coefficient C<inf>9</inf> in MC (Monte Carlo) simulations of B⁰ → K^∗ μ⁺ μ⁻ decays. The technique described here can be generalized and may find applicability across various HEP experiments and elsewhere.