  Fast, efficient and accurate triggers are a critical requirement for modern
high-energy physics experiments given the increasingly large quantities of data
that they produce. The CEBAF Large Acceptance Spectrometer (CLAS12) employs a
highly efficient electron trigger to filter the amount of recorded data by
requiring at least one electron in each event, at the cost of a low purity in
electron identification. Machine learning algorithms are increasingly employed
for classification tasks such as particle identification due to their high
accuracy and fast processing times. In this article, we show how a
convolutional neural network could be deployed as a Level 3 electron trigger at
CLAS12. We demonstrate that the AI trigger would achieve a significant data
reduction compared to the traditional trigger, whilst preserving a 99.5\%
electron identification efficiency. The AI trigger purity as a function of
increased luminosity is improved relative to the traditional trigger. As a
consequence, this AI trigger can achieve a data recording reduction improvement
of 0.33\% per nA when compared to the traditional trigger whilst maintaining an
efficiency above 99.5\%. A reduction in data output also reduces storage costs
and post-processing times, which in turn reduces the time to the publication of
new physics measurements.
