Training Deep 3D Convolutional Neural Networks to Extract BSM Physics Parameters Directly from HEP Data: a Proof-of-Concept Study Using Monte Carlo Simulations

• URL: http://arxiv.org/abs/2311.13060v3
• Date: Fri, 15 Nov 2024 16:55:47 GMT
• Title: Training Deep 3D Convolutional Neural Networks to Extract BSM Physics Parameters Directly from HEP Data: a Proof-of-Concept Study Using Monte Carlo Simulations
• Authors: S. Dubey, T. E. Browder, S. Kohani, R. Mandal, A. Sibidanov, R. Sinha,
• Abstract summary: We propose a simple but novel data representation that transforms the angular and kinematic distributions into "quasi-images" 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 $B0 rightarrow K*0mu+mu-$ decays.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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