Deep Learning Level-3 Electron Trigger for CLAS12

TL;DR

This paper proposes a convolutional neural network-based Level-3 electron trigger for CLAS12 that significantly reduces data volume while maintaining high electron identification efficiency, improving data purity and reducing costs.

Contribution

It introduces a novel AI-based trigger system for CLAS12 that outperforms traditional methods in data reduction and purity, with high efficiency.

Findings

Achieves 99.5% electron identification efficiency

Reduces data recording by 0.33% per nA compared to traditional trigger

Improves trigger purity at higher luminosities

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 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.