End-to-End Pipeline for Trigger Detection on Hit and Track Graphs
- Stony Brook University, NY (United States)
- Sunrise Technology Inc., Stony Brook, NY (United States)
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- New Jersey Institute of Technology, Newark, NJ (United States)
There has been a surge of interest in applying deep learning in particle and nuclear physics to replace labor-intensive offline data analysis with automated online machine learning tasks. This paper details a novel AI-enabled triggering solution for physics experiments in Relativistic Heavy Ion Collider and future Electron-Ion Collider. The triggering system consists of a comprehensive end-to-end pipeline based on Graph Neural Networks that classifies trigger events versus background events, makes online decisions to retain signal data, and enables efficient data acquisition. Here, the triggering system first starts with the coordinates of pixel hits lit up by passing particles in the detector, applies three stages of event processing (hits clustering, track reconstruction, and trigger detection), and labels all processed events with the binary tag of trigger versus background events. By switching among different objective functions, we train the Graph Neural Networks in the pipeline to solve multiple tasks: the edge-level track reconstruction problem, the edge-level track adjacency matrix prediction, and the graph-level trigger detection problem. We propose a novel method to treat the events as track-graphs instead of hit-graphs. This method focuses on intertrack relations and is driven by underlying physics processing. As a result, it attains a solid performance (around 72% accuracy) for trigger detection and outperforms the baseline method using hit-graphs by 2% higher accuracy.
- Research Organization:
- Sunrise Technology Inc., Stony Brook, NY (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- Grant/Contract Number:
- SC0019518
- OSTI ID:
- 2000447
- Journal Information:
- Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37, Issue 13; ISSN 2159-5399
- Publisher:
- Association for the Advancement of Artificial IntelligenceCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Similar Records
Track Seeding and Labelling with Embedded-space Graph Neural Networks
Charged Particle Tracking via Edge-Classifying Interaction Networks