2025/06/24 by Xiong, Ziyao, Qixing Deng, Deng, Qixing +3
Biochemistry, Genetics and Molecular Biology · #FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph) #Machine Learning in Bioinformatics
paper · pdf · doi:10.48550/arxiv.2506.19501
openalex publication_date 2025/06/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This study investigates the rare decay B0 -> K*0 tau+ tau-, which is sensitive to potential violations of lepton flavor universality predicted by the Standard Model. A Monte Carlo simulated dataset containing both signal and the dominant background process B0 -> K*0 D+ D- was used to train and evaluate machine learning classifiers. After feature selection and parameter tuning, two supervised models -- Boosted Decision Trees (BDTs) and Fully Connected Neural Networks (FCNNs) -- were trained. Feature engineering was then applied to enhance classification performance. On the test set, the BDT achieved an AUC of 0.912 +/- 0.000 and an F1-score of 0.828 +/- 0.001, while the FCNN reached an AUC of 0.877 +/- 0.000 and an F1-score of 0.799 +/- 0.001. These results demonstrate that both models can robustly separate signal from background in rare decay searches, supporting their application in future LHCb analyses.