The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector
The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector
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DOI：
10.1140/epjc/s10052-017-5481-6
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发表时间：
2018-01-29
影响因子：
4.4
通讯作者：
Zhang, C.
中科院分区：
文献类型：
--
作者：
Acciarri, R.;Adams, C.;Zhang, C.
用于中微子物理学的液氩时间投影室的开发和操作产生了对模式识别的新方法的需求，以充分利用该技术提供的成像能力。虽然人类大脑可以擅长识别记录事件中的特征，但开发自动化的算法解决方案是一个重大挑战。Pandora软件开发工具包提供了帮助设计和实现模式识别算法的功能。它促进使用多算法方法进行模式识别，其中每个算法都处理特定拓扑中的特定任务。然后，数十种算法仔细地构建了事件的图像，并共同提供了一个强大的自动模式识别解决方案。本文详细介绍了超过100潘多拉的算法和工具，用于重建宇宙射线μ子和中微子的MicroBooNE探测器事件链的细节。评估当前模式识别性能的模拟MicroBooNE事件，使用选择的最终状态事件拓扑结构。
The development and operation of liquid-argon time-projection chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.