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Many scientific applications from rare-event searches to condensed mattersystem characterization to high-rate nuclear experiments require time-domaintriggering on a raw stream of data, where the triggering is generallythreshold-based or randomly acquired. When carrying out detector R&D, there isa need for a general data acquisition (DAQ) system to quickly and efficientlyprocess such data. In the SPLENDOR collaboration, we are developing thePython-based SPLENDAQ package for this exact purpose - it offers two mainfeatures for offline analysis of continuous data: a threshold-triggeringalgorithm based on the time-domain optimal filter formalism and an algorithmfor randomly choosing nonoverlapping segments for noise measurements. Combinedwith the commercially available Moku platform, developed by Liquid Instruments,we have a full pipeline of event building off raw data with minimal setup.Here, we review the underlying principles of this detector-agnostic DAQ packageand give concrete examples of its utility in various applications.

SPLENDAQ: A Detector-Agnostic Data Acquisition System for Small-Scale Physics Experiments