Abstract
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.
Highlights
In modern high energy and nuclear physics experiments data are costly to acquire
Failure to adequately monitor incoming data quality may result in the collection of sub-physics quality data, hindering all downstream scientific processes
Shift crews are tasked with regularly monitoring a rotating set of histograms produced from the accumulating statistics of each run
Summary
In modern high energy and nuclear physics experiments data are costly to acquire This makes data quality monitoring an increasingly essential component. Shift crews (2 individuals) are responsible for: looking over these plots regularly, responding to audible alarms, and contacting experts as needed This responsibility is in addition to experimental configuration changes and normal data acquisition tasks. The alarm system though has limited knowledge as to the intent of researchers and there are many fault conditions which can and do go unnoticed One such event that occurred during a GlueX run was a red light in a dark room being mistakenly left on after component repairs. It was a fairly subtle effect that showed up as an increase in the occupancy of the calorimeter blocks that were away from the beam-line This went unnoticed by shift crews and no alarms were produced. The entire process is labor intensive requiring many humans to operate in a regime in which mistakes are likely
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