Book ; Online: Training Deep 3D Convolutional Neural Networks to Extract BSM Physics Parameters Directly from HEP Data
a Proof-of-Concept Study Using Monte Carlo Simulations
2023
Abstract: 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- ... ...
Abstract | 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. |
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Keywords | High Energy Physics - Experiment ; Computer Science - Machine Learning ; High Energy Physics - Phenomenology |
Subject code | 612 |
Publishing date | 2023-11-21 |
Publishing country | us |
Document type | Book ; Online |
Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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