Speaker
Description
Abstract
Streaming readout (SRO) data acquisition is emerging as a key paradigm for high-luminosity nuclear and particle physics experiments, enabling triggerless operation with continuous, time-stamped data streams processed in software. By reorganizing detector hits into time slices and performing real-time reconstruction and selection using global detector information, SRO allows efficient data reduction and the integration of advanced AI-based algorithms on heterogeneous computing platforms. This approach has been adopted by several future experiments, including SOLID at Jefferson Lab and ePIC at the Electron–Ion Collider.
At Jefferson Lab, SRO architectures based on distributed microservices are being developed and validated through dedicated testbed activities under realistic operating conditions. These testbeds are used to assess system scalability, timing synchronization, network throughput, and real-time data reduction strategies, including both conventional compression and machine-learning–based methods. In particular, the STREAM-AI framework provides a flexible platform for prototyping and benchmarking smart online algorithms, such as autoencoder-based compression and AI-assisted filtering, across the full DAQ chain. These efforts inform the design of robust, scalable, and AI-enabled DAQ systems for next-generation high-rate experiments such as ePIC.
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