Current and future experiments at the high intensity frontier are expected to
produce an enormous amount of data that needs to be collected and stored for
offline analysis. Thanks to the continuous progress in computing and networking
technology, it is now possible to replace the standard triggered' data acquisition systems with a new, simplified and outperforming scheme. Streaming
readout’ (SRO) DAQ aims to replace the hardware-based trigger with a much more
powerful and flexible software-based one, that considers the whole detector
information for efficient real-time data tagging and selection. Considering the
crucial role of DAQ in an experiment, validation with on-field tests is
required to demonstrate SRO performance. In this paper we report results of the
on-beam validation of the Jefferson Lab SRO framework. We exposed different
detectors (PbWO-based electromagnetic calorimeters and a plastic scintillator
hodoscope) to the Hall-D electron-positron secondary beam and to the Hall-B
production electron beam, with increasingly complex experimental conditions. By
comparing the data collected with the SRO system against the traditional DAQ,
we demonstrate that the SRO performs as expected. Furthermore, we provide
evidence of its superiority in implementing sophisticated AI-supported
algorithms for real-time data analysis and reconstruction.

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