Training 3D ResNets to Extract BSM Physics Parameters from Simulated Data

TL;DR

This paper introduces a novel computer vision approach using 3D ResNets to extract BSM physics parameters from simulated high energy physics data by transforming it into images for neural network regression.

Contribution

It presents a new data representation and applies 3D ResNets to directly regress BSM parameters from high energy physics data, demonstrating a novel application of deep learning in this field.

Findings

Successfully regressed Wilson Coefficient $C_{9}$ from simulated decay data.

Demonstrated the feasibility of using quasi-image representations for physics parameter extraction.

Potential for broad applicability across various high energy physics experiments.

Abstract

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 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.