| Autor: |
Zhang, Yulei, Mo, Cen, Chen, Xiang, Li, Bingzhi, Chen, Hongyang, Hu, Jifeng, Li, Liang |
| Rok vydání: |
2024 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
Long-lived particles (LLPs) provide an unambiguous signal for physics beyond the Standard Model (BSM). They have a distinct detector signature, with decay lengths corresponding to lifetimes of around nanoseconds or longer. Lepton colliders allow LLP searches to be conducted in a clean environment, and such searches can reach their full physics potential when combined with machine learning (ML) techniques. In the case of LLPs searches from Higgs decay in $e^+e^-\to ZH$, we show that the LLP signal efficiency can be improved up to 99% with an LLP mass around 50 GeV and a lifetime of approximately $1$ nanosecond, using deep neural network based approaches. The signal sensitivity for the branching ratio of Higgs decaying into LLPs reaches $1.2 \times 10^{-6}$ with a statistics of $4 \times 10^{6}$ Higgs. |
| Databáze: |
arXiv |
| Externí odkaz: |
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