ŠIROKÝ, Filip. Anomaly Detection Using Deep Sparse Autoencoders for CERN Particle Detector Data. 2019.
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Basic information
Original name Anomaly Detection Using Deep Sparse Autoencoders for CERN Particle Detector Data
Authors ŠIROKÝ, Filip (203 Czech Republic, guarantor, belonging to the institution).
Edition 2019.
Other information
Original language English
Type of outcome Presentations at conferences
Field of Study 10200 1.2 Computer and information sciences
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
WWW Scientific poster
RIV identification code RIV/00216224:14330/19:00109515
Organization unit Faculty of Informatics
Keywords (in Czech) umělá inteligence; strojové učení; hluboké učení; detekce anomálií; detektory ionizujícího záření
Keywords in English artificial intelligence; machine learning; deep learning; anomaly detection; particle detectors
Tags machine learning
Tags International impact
Changed by Changed by: RNDr. Vít Starý Novotný, Ph.D., učo 409729. Changed: 24/4/2019 23:27.
Abstract

Our work is a scientific poster that was presented at the ML Prague 2019 conference during February 22–24, 2019.

The certification of the CMS particle detector data, as usable for physics analysis, is a crucial task to ensure the quality of all physics results published by CERN. Currently, the certification conducted by human experts is labor intensive and can only be segmented on a long period of time basis.

This contribution focuses on the design and prototype of an automated certification system assessing data quality on a per-luminosity section (i.e. 23 seconds of data taking) basis. Anomalies caused by detector malfunctions or sub-optimal reconstruction are unpredictable and occur rarely, making it difficult to use classical supervised classification methods such as feedforward neural networks. We base our prototype on a semi-supervised model which employs deep sparse autoencoders. This approach has been qualified successfully on CMS data collected during the 2016 LHC run: we demonstrate its ability to detect anomalies with high accuracy and low false positive rate when compared against the manual certification by experts. A key advantage of this approach over other ML technologies is having great interpretability of the results, which can be further used to ascribe the origin of the problems in the data to a specific sub-detector or particle physics objects.

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