在CMS检测器中用于自动化数据质量监测的异常检测
Andrew Brinkerhoff1,2, Chosila Sutantawibul1, Indara Suarez3
1Baylor University, Waco, USA.
概括
自动数据质量监测 (AutoDQM) 使用机器学习快速评估数据质量,用于紧的电磁体 (CMS) 实验. 该系统有效地识别了故障的探测器数据,提高了物理分析的可靠性.
科学领域:
- 高能物理学的高能物理学
- 数据科学是数据科学.
- 探测器仪器仪表仪表的检测器仪表.
背景情况:
- 大型粒子探测器的有效运行,例如CERN大型强子对撞机的紧型子索莱尼顿 (CMS),需要快速和彻底的数据质量评估.
- 传统方法可能耗时,可能无法捕捉微妙的异常.
研究的目的:
- 引入和评估用于自动化数据质量监控的AutoDQM系统.
- 提高高能物理实验中识别异常数据的效率和准确性.
主要方法:
- 使用先进的统计技术和无监督机器学习开发AutoDQM系统.
- 实施异常检测算法,包括β-双项概率函数和主要组件分析.
- 在2022年对CMS的质子对质子碰撞数据的完整数据集进行测试.
主要成果:
- 自动DQM成功识别了由检测器故障引起的异常"坏"数据.
- 该系统显示",坏"数据的检测率是"好"数据的4-6倍.
- 验证了AutoDQM作为一般数据质量监测工具的有效性.
结论:
- 在复杂的粒子物理实验中,AutoDQM为实时数据质量监测提供了强大而高效的解决方案.
- 该系统检测异常的能力有助于确保科学结果的完整性.
- 自动DQM在管理和验证来自碰撞机实验的大数据集方面取得了重大进展.
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