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DOE OSTI · 2282542

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

Watkins, Samuel Linton
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Abstract

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. Further, 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.

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75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITYatomicdark matterdata acquisitiondetectornuclear and particle physicsoptimal filter

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Watkins, Samuel Linton. 2023-12-22. SPLENDAQ: A Detector-Agnostic Data Acquisition System for Small-Scale Physics Experiments. https://doi.org/10.1007/s10909-023-03021-w

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