Speaker
Description
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 Level 3 electron trigger to filter the amount of data recorded by requiring at least one electron in each event, at the cost of a low purity in electron identification. However, 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 also improves relative to the traditional trigger with increased luminosity, as the AI trigger can achieve a reduction in recorded data with respect to the traditional trigger that increases at a rate of 0.32% per nA whilst keeping a stable efficiency above 99.5%.
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