| Issue |
EPJ Web Conf.
Volume 251, 2021
25th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2021)
|
|
|---|---|---|
| Article Number | 04010 | |
| Number of page(s) | 7 | |
| Section | Online Computing | |
| DOI | https://doi.org/10.1051/epjconf/202125104010 | |
| Published online | 23 August 2021 | |
https://doi.org/10.1051/epjconf/202125104010
AI Enabled Data Quality Monitoring with Hydra
Thomas Jefferson National Accelerator Facility
* e-mail: tbritton@jlab.org
** e-mail: davidl@jlab.org
*** e-mail: kishan@jlab.org
Published online: 23 August 2021
Data quality monitoring is critical to all experiments impacting the quality of any physics results. Traditionally, this is done through an alarm system, which detects low level faults, leaving higher level monitoring to human crews. Artificial Intelligence is beginning to find its way into scientific applications, but comes with difficulties, relying on the acquisition of new skill sets, either through education or acquisition, in data science. This paper will discuss the development and deployment of the Hydra monitoring system in production at Gluex. It will show how “off-the-shelf” technologies can be rapidly developed, as well as discuss what sociological hurdles must be overcome to successfully deploy such a system. Early results from production running of Hydra will also be shared as well as a future outlook for development of Hydra.
© The Authors, published by EDP Sciences, 2021
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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