Search for Long-lived Particles at Future Lepton Colliders Using Deep Learning Techniques (2401.05094v4)
Abstract: 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 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.
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- The total invisible decay of LLPs (type III signal) is not considered in this analysis.
- Detectable means that LLPs have visible decay products within the geometric acceptance of the detector.
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