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The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

机译:pandora多算法的自动模式识别方法   microBooNE探测器中的宇宙射线μ子和中微子事件

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摘要

The development and operation of Liquid-Argon Time-Projection Chambers forneutrino physics has created a need for new approaches to pattern recognitionin order to fully exploit the imaging capabilities offered by this technology.Whereas the human brain can excel at identifying features in the recordedevents, it is a significant challenge to develop an automated, algorithmicsolution. The Pandora Software Development Kit provides functionality to aidthe design and implementation of pattern-recognition algorithms. It promotesthe use of a multi-algorithm approach to pattern recognition, in whichindividual 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 paperdescribes details of the chain of over one hundred Pandora algorithms and toolsused to reconstruct cosmic-ray muon and neutrino events in the MicroBooNEdetector. Metrics that assess the current pattern-recognition performance arepresented for simulated MicroBooNE events, using a selection of final-stateevent topologies.
机译:中微子物理学的液态氩时间投影室的开发和运行,需要一种新的模式识别方法,以充分利用该技术提供的成像功能。人脑在识别事件中的特征方面表现出色,开发自动化的算法解决方案是一项重大挑战。 Pandora软件开发套件提供的功能可帮助设计和实施模式识别算法。它促进了使用多算法模式识别的方法,其中每种算法分别解决特定拓扑中的特定任务。然后数十种算法仔细地构建事件的图片,并一起提供强大的自动模式识别解。本文介绍了用于重建MicroBooNEdetector中的宇宙射线μ子和中微子事件的100多种Pandora算法和工具的链的详细信息。使用选定的最终状态事件拓扑,为模拟的MicroBooNE事件提供了评估当前模式识别性能的指标。

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