Deep learning level-3 electron trigger for CLAS12

Abstract

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 Artificial Intelligence (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 about 65% at standard CLAS12 luminosities when compared to the traditional trigger. A reduction in data output also reduces storage costs and post-processing times, which in turn reduces time to publication of new physics measurements.