Abstract
Daily operation of a large-scale experiment is a resource consuming task, particularly from perspectives of routine data quality monitoring. Typically, data comes from different sub-detectors and the global quality of data depends on the combinatorial performance of each of them. In this paper, the problem of identifying channels in which anomalies occurred is considered. We introduce a generic deep learning model and prove that, under reasonable assumptions, the model learns to identify ‘channels’ which are affected by an anomaly. Such model could be used for data quality manager cross-check and assistance and identifying good channels in anomalous data samples. The main novelty of the method is that the model does not require ground truth labels for each channel, only global flag is used. This effectively distinguishes the model from classical classification methods. Being applied to CMS data collected in the year 2010, this approach proves its ability to decompose anomaly by separate channels.
Highlights
Data quality monitoring is a crucial task for every large scale high energy physics experiment
Data comes from different sub-detectors and the global quality of data depends on the combinatorial performance of each of them
We introduce a generic deep learning model and prove that, under reasonable assumptions, the model learns to identify ’channels’ which are affected by an anomaly
Summary
Data quality monitoring is a crucial task for every large scale high energy physics experiment. [1], we designed a system, which automatically classifies marginal cases in general: both of ’good’ and ’bad’ data, and use human expert decision to classify remaining ’grey area’ cases. Data comes from different sub-detectors or other subsystems, and the global data quality depends on the combinatorial performance of each of them. We aim instead to determine which sub-detector is responsible for anomaly in the detector behaviour, knowing only global flag. We use data [2] acquired by CMS detector [3] at CERN LHC. Data from the channels, which are reconstructed relying primarily on normally operating sub-detectors, can be used for further specific physics analysis
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