Dubey, S.S.DubeyBrowder, T. E.T. E.BrowderKohani, S.S.KohaniMandal, R.R.MandalSibidanov, A.A.SibidanovSinha, R.R.Sinha2025-08-312025-08-312025-04-292-s2.0-105004803755http://repository.iitgn.ac.in/handle/IITG2025/28166We 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.falseConference Paper1824803929 April 202501034cpConference Proceeding