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Converting existing optical detectors into fast x-ray detectors

Here, the very short burn time and small size of burning plasmas created at advanced laser-fusion facilities will require high-spatial-resolution imaging diagnostics with fast time resolution. These instruments will need to function in an environment of extremely large neutron fluxes that will cause conventional diagnostics to fail because of radiation damage and induced background levels. One solution to this challenge is to perform an ultrafast conversion of the x-ray signals into the optical regime before the neutrons are able to reach the detector and then to relay image the signal out of the chamber and into a shielded bunker, protected from the effects of these neutrons. With this goal in mind, the OMEGA laser was used to demonstrate high-temporal-resolution x-ray imaging by using an x-ray snout to image an imploding backlighter capsule onto a semiconductor. The semiconductor was simultaneously probed with the existing velocity interferometry system for any surface reflector (VISAR) diagnostic, which uses an optical streak camera and provided a one-dimensional image of the phase in the semiconductor as a function of time. The phase induced in the semiconductor was linearly proportional to the x-ray emission from the backlighter capsule. This approach would then allow a sacrificial semiconductor to be attached at the end of an optical train with the VISAR and optical streak camera placed in a shielded bunker to operate in a high neutron environment and obtain time-dependent one-dimensional x-ray images or time-dependent x-ray spectra from a burning plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

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

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.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗