B0 -> K*0 tau+ tau- Decay: Using Machine Learning to Separate Signal from Background
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866918080691568640 |
|---|---|
| author | Xiong, Ziyao Deng, Qixing Sun, Yidan Yang, Junhua |
| author_facet | Xiong, Ziyao Deng, Qixing Sun, Yidan Yang, Junhua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19501 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | B0 -> K*0 tau+ tau- Decay: Using Machine Learning to Separate Signal from Background Xiong, Ziyao Deng, Qixing Sun, Yidan Yang, Junhua High Energy Physics - Phenomenology 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. |
| title | B0 -> K*0 tau+ tau- Decay: Using Machine Learning to Separate Signal from Background |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2506.19501 |