Muon identification with Deep Neural Network in the Belle II K-Long and Muon detector
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911080806088704 |
|---|---|
| author | Wang, Zihan Sato, Yo Ishikawa, Akimasa Ushiroda, Yutaka Uno, Kenta Sumisawa, Kazutaka Baghel, Naveen Kumar Choudhury, Seema De Pietro, Giacomo Ketter, Christopher Kindo, Haruki Lam, Tommy Meier, Frank Prell, Soeren |
| author_facet | Wang, Zihan Sato, Yo Ishikawa, Akimasa Ushiroda, Yutaka Uno, Kenta Sumisawa, Kazutaka Baghel, Naveen Kumar Choudhury, Seema De Pietro, Giacomo Ketter, Christopher Kindo, Haruki Lam, Tommy Meier, Frank Prell, Soeren |
| contents | Muon identification is crucial for elementary particle physics experiments. At the Belle II experiment, muons and pions with momenta greater than 0.7 GeV/c are distinguished by their penetration ability through the $K_L$ and Muon (KLM) sub-detector, which is the outermost sub-detector of Belle II. In this paper, we first discuss the possible room for $μ/π$ identification performance improvement and then present a new method based on Deep Neural Network (DNN). This DNN model utilizes the KLM hit pattern variables as the input and thus can digest the penetration information better than the current algorithm. We test the new method in simulation and find that the pion fake rate (specificity) is reduced from 4.1% to 1.6% at a muon efficiency (recall) of 90%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11351 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Muon identification with Deep Neural Network in the Belle II K-Long and Muon detector Wang, Zihan Sato, Yo Ishikawa, Akimasa Ushiroda, Yutaka Uno, Kenta Sumisawa, Kazutaka Baghel, Naveen Kumar Choudhury, Seema De Pietro, Giacomo Ketter, Christopher Kindo, Haruki Lam, Tommy Meier, Frank Prell, Soeren High Energy Physics - Experiment Instrumentation and Detectors Muon identification is crucial for elementary particle physics experiments. At the Belle II experiment, muons and pions with momenta greater than 0.7 GeV/c are distinguished by their penetration ability through the $K_L$ and Muon (KLM) sub-detector, which is the outermost sub-detector of Belle II. In this paper, we first discuss the possible room for $μ/π$ identification performance improvement and then present a new method based on Deep Neural Network (DNN). This DNN model utilizes the KLM hit pattern variables as the input and thus can digest the penetration information better than the current algorithm. We test the new method in simulation and find that the pion fake rate (specificity) is reduced from 4.1% to 1.6% at a muon efficiency (recall) of 90%. |
| title | Muon identification with Deep Neural Network in the Belle II K-Long and Muon detector |
| topic | High Energy Physics - Experiment Instrumentation and Detectors |
| url | https://arxiv.org/abs/2503.11351 |