论文标题
电磁训练仪的深度学习研究
Deep learning study of an electromagnetic calorimeter
论文作者
论文摘要
从现代粒子物理检测器（例如电磁量热计）中准确而精确的信息可能是复杂且具有挑战性的。为了克服困难，我们建议使用深度学习方法处理检测器输出。我们的算法方法利用了已知的网络体系结构，该架构正在修改以适应手头的问题。结果是高质量的（偏差为2％），此外，表明大多数信息可能仅来自检测器的一小部分。我们得出的结论是，这种分析有助于我们理解检测器的基本机制，应作为其设计程序的一部分进行。
The accurate and precise extraction of information from a modern particle physics detector, such as an electromagnetic calorimeter, may be complicated and challenging. In order to overcome the difficulties we propose processing the detector output using the deep-learning methodology. Our algorithmic approach makes use of a known network architecture, which is being modified to fit the problems at hand. The results are of high quality (biases of order 2%) and, moreover, indicate that most of the information may be derived from only a fraction of the detector. We conclude that such an analysis helps us understanding the essential mechanism of the detector and should be performed as a part of its designing procedure.
