Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Detector monitoring with artificial neural networks at the CMS
  experiment at the CERN Large Hadron Collider   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.

20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph