Abstract
Certifying the data recorded by the Compact Muon Solenoid (CMS) experiment at CERN is a crucial and demanding task as the data is used for publication of physics results. Anomalies caused by detector malfunctioning or sub-optimal data processing are difficult to enumerate a priori and occur rarely, making it difficult to use classical supervised classification. We base out prototype towards the automation of such procedure on a semi-supervised approach using deep autoencoders. We demonstrate the ability of the model to detect anomalies with high accuracy, when compared against the outcome of the fully supervised methods. We show that the model has great interpretability of the results, ascribing the origin of the problems in the data to a specific sub-detector or physics object. Finally, we address the issue of feature dependency on the LHC beam intensity.
Highlights
The Compact Muon Solenoid (CMS) experiment is one of the two general purpose experiments at the Large Hadron Collider (LHC)
Certifying the data recorded by the Compact Muon Solenoid (CMS) experiment at CERN is a crucial and demanding task as the data is used for publication of physics results
The CMS detector is a complex apparatus composed of several sub-detectors, each of them specialized in the measuring the properties of a particular kind of particles
Summary
The CMS experiment is one of the two general purpose experiments at the Large Hadron Collider (LHC). The CMS detector is a complex apparatus composed of several sub-detectors, each of them specialized in the measuring the properties of a particular kind of particles. The data acquired by the experiment are scrutinized by a procedure called data certification (DC) which ensures they are usable for all physics analysis. This procedure is the last step of the complex Data Quality Monitoring (DQM) [2] apparatus of the experiment. The current certification procedure is conducted by experts of the various sub-detectors and is based on histograms of the relevant quantities which are monitored, at various stages of the data-processing infrastructure, via the DQM setup
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