Trigger Detection for the sPHENIX Experiment via Bipartite Graph Networks with Set Transformer
- 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)
Trigger (interesting events) detection is crucial to high-energy and nuclear physics experiments because it improves data acquisition efficiency. It also plays a vital role in facilitating the downstream offline data analysis process. The sPHENIX detector, located at the Relativistic Heavy Ion Collider in Brookhaven National Laboratory, is one of the largest nuclear physics experiments on a world scale and is optimized to detect physics processes involving charm and beauty quarks. Furthermore, these particles are produced in collisions involving two proton beams, two gold nuclei beams, or a combination of the two and give critical insights into the formation of the early universe. This paper presents a model architecture for trigger detection with geometric information from two fast silicon detectors. Transverse momentum is introduced as an intermediate feature from physics heuristics. We also prove its importance through our training experiments. Each event consists of tracks and can be viewed as a graph. A bipartite graph neural network is integrated with the attention mechanism to design a binary classification model. Compared with the state-of-the-art algorithm for trigger detection, our model is parsimonious and increases the accuracy and the AUC score by more than 15%.
- 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:
- 2000449
- Journal Information:
- Lecture Notes in Computer Science, Vol. 13715; ISSN 0302-9743
- Publisher:
- SpringerCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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