Speaker
Description
Streaming readout (SRO) data acquisition is emerging as a key paradigm for high-luminosity nuclear and particle physics experiments, enabling triggerless operation through continuous, time-stamped data streams processed in software. By organizing detector hits into time slices and performing real-time reconstruction and selection using global detector information, SRO enables efficient data reduction and the use of advanced AI-based algorithms on heterogeneous computing platforms.
This approach is being adopted by several experiments, including CLAS12 at Jefferson Lab (in its upgrade phase) and ePIC at the Electron-Ion Collider. At Jefferson Lab, SRO architectures based on distributed microservices are under active development and validation through dedicated testbed activities carried out under realistic operating conditions. These testbeds are used to evaluate system scalability, timing synchronization, network throughput, and real-time data reduction strategies, including both conventional compression techniques and machine-learning-based methods.
In the following, representative examples of these testbeds will be presented to illustrate the design choices and performance of such systems. These studies contribute to the development of robust, scalable, and AI-enabled DAQ architectures for next-generation high-rate experiments such as ePIC.