| Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
|
|
|---|---|---|
| Article Number | 09024 | |
| Number of page(s) | 6 | |
| Section | Artificial Intelligence and Machine Learning | |
| DOI | https://doi.org/10.1051/epjconf/202429509024 | |
| Published online | 06 May 2024 | |
https://doi.org/10.1051/epjconf/202429509024
Machine Learning for New Physics Searches in B → K*0µ+µ− Decays
1 University of Hawai‘i at Mānoa, Honolulu, HI 96822, USA
2 Indian Institute of Technology Gandhinagar, Gujarat 382055, India
* e-mail: sdubey@hawaii.edu
Published online: 6 May 2024
We report the status of a neural network regression model trained to extract new physics (NP) parameters in Monte Carlo (MC) simulation data. We utilize a new EvtGen NP MC generator to generate B → K*0µ+µ− events according to the deviation of the Wilson Coefficient C9 from its SM value, δC9. We train a three-dimensional ResNet regression model, using images built from the angular observables and the invariant mass of the di-muon system, to extract values of δ C9 directly from the MC data samples. This work is intended for future analyses at the Belle II experiment but may also find applicability at other experiments.
© The Authors, published by EDP Sciences, 2024
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