Speaker
Description
This R&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiments (RHIC, LHC, and future EIC). Our focus is on developing a demonstrator for real-time processing of high-rate data streams from sPHENIX experiment tracking detectors. Integrating streaming readout and intelligent control with FPGA, the approach efficiently identifies rare heavy flavor events in high-rate p+p collisions (3MHz) within limited DAQ bandwidth (15kHz), using GNN and hls4ml. Success at sPHENIX promises immediate benefits, minimizing resources and accelerating the heavy-flavor measurements. The approach is transferable to other fields. For the EIC, we develop a DIS-electron tagger using AI-ML algorithms for real-time identification, showcasing the transformative potential of AI and FPGA technologies in high-energy nuclear and particle experiments real-time data processing pipelines.
| Alternate track | 13. Detectors for Future Facilities, R&D, Novel Techniques |
|---|---|
| I read the instructions above | Yes |
