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arXiv 1906.09058hep-ex
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在LHC上使用机器学习实时鉴别光子对

Real-time discrimination of photon pairs using machine learning at the LHC

Sean Benson, Adrián Casais Vidal, Xabier Cid Vidal, Albert Puig Navarro

摘要:

ALP介导的衰变和其他尚未观察到的$B$衰变到双光子终态是一个挑战,选择在强子对撞机环境中,由于大的背景,直接来自$pp$碰撞。我们提出了2018年LHCb实验实施的策略,以有效地选择这样的光子对。在LHCb实时选择框架中实现的快速神经网络拓扑在$4-20$ GeV $/c^{2}$的质量范围内实现了高效率。我们讨论了LHCb实验的影响和未来前景。

英文摘要:

ALP-mediated decays and other as-yet unobserved $B$ decays to di-photon final states are a challenge to select in hadron collider environments due to the large backgrounds that come directly from the $pp$ collision. We present the strategy implemented by the LHCb experiment in 2018 to efficiently select such photon pairs. A fast neural network topology, implemented in the LHCb real-time selection framework achieves high efficiency across a mass range of $4-20$ GeV$/c^{2}$. We discuss implications and future prospects for the LHCb experiment.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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