Intelligent experiments through real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and future EIC detectors

TL;DR

This paper presents an AI-driven real-time data processing system for high-energy nuclear experiments, utilizing streaming technology, GNNs, and FPGA-based ML to improve event detection and trigger rates in sPHENIX and future EIC detectors.

Contribution

It introduces a novel AI-based demonstrator for real-time high-rate data processing, combining streaming tech, GNNs, and FPGA acceleration for nuclear physics experiments.

Findings

Successful real-time identification of rare heavy flavor events

Negation of calorimeter trigger rate limitations using AI and streaming

Transferable approach applicable to other high-energy physics experiments

Abstract

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. The limitations of a 15 kHz maximum trigger rate imposed by the calorimeters can be negated by intelligent use of streaming technology in the tracking system. The approach efficiently identifies low momentum rare heavy flavor events in high-rate p+p collisions (3MHz), using Graph Neural Network (GNN) and High Level Synthesis for Machine Learning (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 Artificial Intelligence - Machine Learning (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.