17–24 Jul 2024
Prague
Europe/Prague timezone

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

18 Jul 2024, 19:00
2h
Foyer Floor 2

Foyer Floor 2

Poster 14. Computing, AI and Data Handling Poster Session 1

Speaker

Dr Shawn Dubey (Brown University)

Description

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.

Alternate track 05. Quark and Lepton Flavour Physics
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Author

Dr Shawn Dubey (Brown University)

Co-authors

Dr Alexei Sibidanov (University of Hawaii at Manoa) Prof. Rahul Sinha (University of Hawaii at Manoa) Dr Rusa Mandal (Indian Institute of Technology Gandhinagar) Dr Shahab Kohani (University of Hawaii at Manoa) Thomas Browder

Presentation materials