标题： 未来轻子对撞机中利用深度学习技术搜索长寿命粒子 显示英文标题

标题： Search for Long-lived Particles at Future Lepton Colliders Using Deep Learning Techniques

摘要： 长寿命粒子（LLPs）为超出标准模型（BSM）的物理提供了明确的信号。 它们具有独特的探测器特征，其衰变长度对应于约纳秒或更长的寿命。 轻子对撞机允许在干净的环境中进行LLP搜索，当与机器学习（ML）技术结合时，可以充分发挥其物理潜力。 这项实验研究利用全面的完整模拟数据样本，专注于来自希格斯衰变的LLP搜索， $e^+e^-\to ZH$。 我们证明，通过采用深度神经网络方法，对于大约50 GeV的LLP质量以及约1纳秒的寿命，LLP信号效率可以提高到95%，同时排斥所有 SM背景。 此外，对于希格斯衰变成LLPs的分支比的信号灵敏度，在统计量为 $4 \times 10^{6}$个希格斯的情况下达到了最先进的极限 $9.7 \times 10^{-7}$。 显示英文摘要

摘要： 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. This experimental study, utilizing comprehensive full simulation data samples, focuses on LLP searches resulting from Higgs decay in $e^+e^-\to ZH$. We demonstrate that, by employing deep neural network approaches the LLP signal efficiency can be improved up to 95% for an LLP mass around 50 GeV and a lifetime of approximately 1 nanosecond, while rejecting all SM backgrounds. Furthermore, the signal sensitivity for the branching ratio of Higgs decaying into LLPs reaches a state-of-art limit of $9.7 \times 10^{-7}$ with a statistics of $4 \times 10^{6}$ Higgs.