论文标题
快速卷积神经网络，用于识别高颗粒量热计中的长寿命
Fast convolutional neural networks for identifying long-lived particles in a high-granularity calorimeter
论文作者
论文摘要
我们提供了第一个概念证明，它可以直接使用基于神经网络的模式识别来触发来自位移粒子的不同热量计特征，例如来自异国情调长寿命颗粒的衰减的粒子。该研究的高粒度向前热量计进行了类似于计划的高粒度量热仪的高光度升级，以升级CMS检测器在CERN大型强子对撞机上的高光度升级。如果没有假设一个预测长寿命颗粒的特定模型，我们表明一个简单的卷积神经网络原则上可以在专用的快速硬件上部署，可以有效地从流离失所的粒子到低势能识别淋浴，同时提供较低的触发速率。
We present a first proof of concept to directly use neural network based pattern recognition to trigger on distinct calorimeter signatures from displaced particles, such as those that arise from the decays of exotic long-lived particles. The study is performed for a high granularity forward calorimeter similar to the planned high granularity calorimeter for the high luminosity upgrade of the CMS detector at the CERN Large Hadron Collider. Without assuming a particular model that predicts long-lived particles, we show that a simple convolutional neural network, that could in principle be deployed on dedicated fast hardware, can efficiently identify showers from displaced particles down to low energies while providing a low trigger rate.
