Detector Monitoring with Artificial Neural Networks at the CMS Experiment at the CERN Large Hadron Collider View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Adrian Alan Pol, Gianluca Cerminara, Cecile Germain, Maurizio Pierini, Agrima Seth

ABSTRACT

Reliable data quality monitoring is a key asset in delivering collision data suitable for physics analysis in any modern large-scale high energy physics experiment. This paper focuses on the use of artificial neural networks for supervised and semi-supervised problems related to the identification of anomalies in the data collected by the CMS muon detectors. We use deep neural networks to analyze LHC collision data, represented as images organized geographically. We train a classifier capable of detecting the known anomalous behaviors with unprecedented efficiency and explore the usage of convolutional autoencoders to extend anomaly detection capabilities to unforeseen failure modes. A generalization of this strategy could pave the way to the automation of the data quality assessment process for present and future high energy physics experiments. More... »

PAGES

3

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s41781-018-0020-1

DOI

http://dx.doi.org/10.1007/s41781-018-0020-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111101013


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