  Many scientific applications from rare-event searches to condensed matter
system characterization to high-rate nuclear experiments require time-domain
triggering on a raw stream of data, where the triggering is generally
threshold-based or randomly acquired. When carrying out detector R&D, there is
a need for a general data acquisition (DAQ) system to quickly and efficiently
process such data. In the SPLENDOR collaboration, we are developing the
Python-based SPLENDAQ package for this exact purpose - it offers two main
features for offline analysis of continuous data: a threshold-triggering
algorithm based on the time-domain optimal filter formalism and an algorithm
for randomly choosing nonoverlapping segments for noise measurements. Combined
with 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 package
and give concrete examples of its utility in various applications.
