利用卷积神经网络对双组分暗物质产生的单X信号进行表征
Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network
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中文总结 AI 辅助
本研究利用卷积神经网络分析大型强子对撞机的单喷注与单Z探测数据，实现对双组分暗物质粒子的存在、质量及自旋的表征，仅为概念验证结果。
中文摘要 AI 辅助
我们评估了卷积神经网络（CNN）在表征大型强子对撞机（LHC）上产生的双组分暗物质（DM）潜在信号的范围，该信号来自单喷注和单Z探测。结果表明，经探测器水平分析后，此类CNN不仅能推断出两个DM粒子的存在，还能提取它们的质量和自旋（自旋为0或1/2）。不过，该结果仅为概念验证，因为我们未开展信号本底分析。
英文摘要
We assess the scope of a Convolutional Neural Network (CNN) in characterizing potential signals of two-component Dark Matter (DM) arising at the Large Hadron Collider (LHC) from mono-jet and mono-Z probes. We show that such a CNN has the ability of not only inferring the presence of two DM particles but also of extracting their mass and spin, the latter being either 0 or 1/2, following detector level analysis. However, such result represents a conceptual proof-of-concept, as we have not entertained a signal-to-background analysis.
