arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2403.04020physics.ins-detnucl-exphysics.data-an
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CLAS12的实时带电径迹重建

Real-Time Charged Track Reconstruction for CLAS12

Gagik Gavalian

AI总结:

本文提出使用机器学习算法从CLAS12漂移室的原始击中数据中实时重建带电粒子径迹,实现了高精度的动量与方向重建及事件拓扑识别,显著提升了核物理实验的数据处理效率并支持流式读出应用。

摘要:

本文介绍了使用人工智能在CLAS12中进行带电粒子径迹重建的结果。在我们的方法中,我们使用机器学习算法,从CLAS12漂移室的原始击中数据中高精度地重建径迹,包括其动量和方向。重建以数据获取的速率实时进行,并允许实时识别事件拓扑结构。这种方法彻底改变了核物理实验的数据处理,使我们能够即时识别和分类实验数据,并将显著减少实验数据处理量。它还可用于流式读出应用,从而实现更高效的数据获取和后处理。

英文摘要:

This paper presents the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use machine learning algorithms to reconstruct tracks, including their momentum and direction, with high accuracy from raw hits of the CLAS12 drift chambers. The reconstruction is performed in real-time, with the rate of data acquisition, and allows for the identification of event topologies in real-time. This approach revolutionizes the Nuclear Physics experiments' data processing, allowing us to identify and categorize the experimental data on the fly, and will lead to a significant reduction in experiment data processing. It can also be used in streaming readout applications leading to more efficient data acquisition and post-processing.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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