Training 3D ResNets to Extract BSM Physics Parameters from Simulated Data
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909729702281216 |
|---|---|
| author | Dubey, S. Browder, T. E. Kohani, S. Mandal, R. Sibidanov, A. Sinha, R. |
| author_facet | Dubey, S. Browder, T. E. Kohani, S. Mandal, R. Sibidanov, A. Sinha, R. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_13060 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Training 3D ResNets to Extract BSM Physics Parameters from Simulated Data Dubey, S. Browder, T. E. Kohani, S. Mandal, R. Sibidanov, A. Sinha, R. High Energy Physics - Experiment Machine Learning High Energy Physics - Phenomenology 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. |
| title | Training 3D ResNets to Extract BSM Physics Parameters from Simulated Data |
| topic | High Energy Physics - Experiment Machine Learning High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2311.13060 |