| Issue |
EPJ Web Conf.
Volume 214, 2019
23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)
|
|
|---|---|---|
| Article Number | 06001 | |
| Number of page(s) | 8 | |
| Section | T6 - Machine learning & analysis | |
| DOI | https://doi.org/10.1051/epjconf/201921406001 | |
| Published online | 17 September 2019 | |
https://doi.org/10.1051/epjconf/201921406001
Monitoring tools for the CMS muon detector: present workflows and future automation
1
INFN
(Bari, Italy)
2
CERN
(Geneva, Switzerland)
* e-mail: cesare.calabria@ba.infn.it
Published online: 17 September 2019
The CMS Muon System has been operated successfully during the two LHC runs allowing to collect a very high fraction of data with a quality that fulfils the requirements to be used for physics analysis. Nevertheless, the workflows used nowadays to operate and monitor the detector are rather expensive in terms of human resources. Focus is therefore being put on improving such workflows, both by applying automated statistical tests and exploiting modern machine learning algorithms, in view of the future LHC runs. The ecosystem of tools presently in use will be presented, together with the state of the art of the developments toward more automatized monitoring and the roadmap for the future.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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