A neural network clustering algorithm for the ATLAS silicon pixel detector
A neural network clustering algorithm for the ATLAS silicon pixel detector
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DOI：
10.1088/1748-0221/9/09/p09009
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发表时间：
2014-06
影响因子：
1.3
通讯作者：
Atlas Collaboration
中科院分区：
文献类型：
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作者：
Atlas Collaboration
提出了一种利用人工神经网络对ATLAS像素探测器中多个带电粒子产生的团簇进行识别和分裂的新方法。这种合并的集群是源自高能物体（如喷流）的轨迹的共同特征。神经网络使用蒙特卡洛样本进行训练，这些样本是通过详细的探测器模拟产生的。该技术取代了以前的聚类方法的基础上的连接组件分析和充电插值。神经网络分裂技术的性能量化使用的数据从质子-质子碰撞在大型强子对撞机收集的ATLAS探测器在2011年和蒙特卡罗模拟。这种技术减少了高能量射流中轨道之间共享的集群数量，最多可减少三倍。它还提供了更精确的位置和误差估计的集群在横向和纵向的影响参数分辨率。
A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. This technique reduces the number of clusters shared between tracks in highly energetic jets by up to a factor of three. It also provides more precise position and error estimates of the clusters in both the transverse and longitudinal impact parameter resolution.