End-To-End Online sPHENIX Trigger Detection Pipeline

This paper will provide a comprehensive end-to-end pipeline to classify triggers verse background events, make online decisions to filter signal data, and enable the intelligent trigger system for efficient data collection in the sPHENIX Data Acquisition System(DAQ). The pipeline starts with the coordinates of pixel hits that are lightened by passing particles in the detector, applies three-stages of event processing (hits clustering, track reconstruction, and trigger detection), and finally, labels all processed events with the binary tag of trigger v.s. background events. The whole pipeline consists of deterministic algorithms such as clustering pixels to reduce event size, tracking reconstruction to predict candidate edges, and advanced graph neural network-based models for recognizing the entire jet pattern. In particular, we apply the Massage Passing Graph Neural Network to predict links between hits and reconstruct tracks and a hierarchical pool algorithm (DiffPool) to make the graph-level trigger detection. We attain an impressive performance ( larger than 70% accuracy) for trigger detection with only 3200 neuron weights in the end-to-end pipeline.