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 to regress on these images and determine the Wilson Coefficient $C_{9}$ in MC (Monte Carlo) simulations of $B \rightarrow K^{*}\mu^{+}\mu^{-}$ decays. The technique described here can be generalized and may find applicability across various HEP experiments and elsewhere.

翻译：我们报告了一种新颖的计算机视觉技术应用，用于直接从高能物理（HEP）味物理数据中提取超出标准模型（BSM）的参数。我们开发了一种将角分布和运动学分布转化为"准图像"的方法，这些图像可用于训练卷积神经网络执行回归任务（类似于拟合）。这与高能物理中通常使用机器学习/人工智能进行的分类功能形成对比。作为概念验证，我们训练了一个34层残差神经网络，对$B \rightarrow K^{*}\mu^{+}\mu^{-}$衰变的蒙特卡罗（MC）模拟中的这些图像进行回归分析，以确定威尔逊系数$C_{9}$。本文描述的技术具有通用性，可能适用于各类高能物理实验及其他领域。