论文标题
使用机器学习将粘合剂量热计中的辐射和电磁相互作用分开
Using machine learning to separate hadronic and electromagnetic interactions in the GlueX forward calorimeter
论文作者
论文摘要
Gluex向前量热计是2800铅玻璃模块的阵列，该阵列被构建以检测Hadrons衰变中产生的光子。该过程的背景源于量热计中的雄激素相互作用，在某些情况下，很难将其与低能光子相互作用区分开。机器学习技术被应用于粘液量量热计中粒子相互作用的分类。使用$ω$梅森的衰减对算法进行了训练，该算法既包含真实的光子和与量热计相互作用的充电颗粒。评估算法的效率，误报率，运行时间和实施复杂性。使用多层感知器神经网的算法被部署在Gluex软件堆栈中，并提供了85％的信号效率，对于中等质量约束，对包含$π^0 $数据样本的背景拒绝为60％。
The GlueX forward calorimeter is an array of 2800 lead glass modules that was constructed to detect photons produced in the decays of hadrons. A background to this process originates from hadronic interactions in the calorimeter, which, in some instances, can be difficult to distinguish from low energy photon interactions. Machine learning techniques were applied to the classification of particle interactions in the GlueX forward calorimeter. The algorithms were trained on data using decays of the $ω$ meson, which contain both true photons and charged particles that interact with the calorimeter. Algorithms were evaluated on efficiency, rate of false positives, run time, and implementation complexity. An algorithm that utilizes a multi-layer perceptron neural net was deployed in the GlueX software stack and provides a signal efficiency of 85% with a background rejection of 60% for an inclusive $π^0$ data sample for an intermediate quality constraint.
