We report on a novel application of computer vision techniques to extract beyond the Standard Model parameters directly from high energy physics flavor data. We propose a simple but novel data representation that transforms the angular and kinematic distributions into "quasi-images", which are used to train a convolutional neural network to perform regression tasks, similar to fitting. As a proof-of-concept, we train a 34-layer Residual Neural Network to regress on these images and determine information about the Wilson Coefficient $C_{9}$ in Monte Carlo simulations of $B^0 \rightarrow K^{*0}\mu^{+}\mu^{-}$ decays. The method described here can be generalized and may find applicability across a variety of experiments.

翻译：我们报告了计算机视觉技术在高能物理味数据中直接提取超越标准模型参数的新颖应用。我们提出了一种简单而新颖的数据表示方法，将角分布和运动学分布转换为“准图像”，用于训练卷积神经网络执行回归任务，类似于拟合过程。作为概念验证，我们训练了一个34层残差神经网络对这些图像进行回归分析，以确定$B^0 \rightarrow K^{*0}\mu^{+}\mu^{-}$衰变蒙特卡洛模拟中威尔逊系数$C_{9}$的相关信息。本文所述方法具有可推广性，有望在多种实验场景中得到应用。