论文标题
学习识别电子
Learning to Identify Electrons
论文作者
论文摘要
我们研究了通常用于区分电子背景和对撞机实验中的电子背景的最新分类特征是否忽略了有价值的信息。将电磁和辐射热量表沉积物的深度卷积神经网络分析与典型特征的性能进行了比较，揭示了$ \％$ \％$差距，这表明这些低级数据确实包含未开发的分类功率。为了揭示这些未使用信息的性质，我们使用最近开发的技术将深层网络映射到具有物理上可解释的可观察到的空间中。我们确定了两个通常不用于电子识别的简单热量计可观测值，但它们模仿了卷积网络的决策并几乎缩小了性能差距。
We investigate whether state-of-the-art classification features commonly used to distinguish electrons from jet backgrounds in collider experiments are overlooking valuable information. A deep convolutional neural network analysis of electromagnetic and hadronic calorimeter deposits is compared to the performance of typical features, revealing a $\approx 5\%$ gap which indicates that these lower-level data do contain untapped classification power. To reveal the nature of this unused information, we use a recently developed technique to map the deep network into a space of physically interpretable observables. We identify two simple calorimeter observables which are not typically used for electron identification, but which mimic the decisions of the convolutional network and nearly close the performance gap.
