Training 3D ResNets to Extract BSM Physics Parameters from Simulated Data
This work addresses the challenge of parameter extraction in particle physics for researchers, but it is incremental as it applies existing computer vision techniques to a new domain.
The paper tackled the problem of extracting beyond the Standard Model physics parameters from high energy physics data by using a novel data representation to train a convolutional neural network for regression, achieving a proof-of-concept in Monte Carlo simulations for determining the Wilson Coefficient C9 in B0 → K*0μ+μ− decays.
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 $B^0 \rightarrow K^{*0}μ^{+}μ^{-}$ decays. The method described here can be generalized and may find applicability across a variety of experiments.