TY - CONF
ID - 1525359
AU - Široký, Filip
PY - 2019
TI - Anomaly Detection Using Deep Sparse Autoencoders for CERN Particle Detector Data
KW - artificial intelligence
KW - machine learning
KW - deep learning
KW - anomaly detection
KW - particle detectors
UR - https://mir.fi.muni.cz/ml-prague-2019/#anomaly-detection
L2 - https://mir.fi.muni.cz/ml-prague-2019/#anomaly-detection
N2 -
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.
ER -
ŠIROKÝ, Filip. \textit{Anomaly Detection Using Deep Sparse Autoencoders for CERN Particle Detector Data}. 2019.