The SPLENDAQ Python Package: A Detector-Agnostic Data Acquisition System for Small-Scale Physics Experiments
ORAL
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. 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.
* This work was supported by the U.S. Department of Energy through the Los Alamos National Laboratory. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of U.S. Department of Energy (Contract No. 89233218CNA000001). Research presented in this work was supported by the Laboratory Directed Research and Development program of Los Alamos National Laboratory under project numbers 20220135DR and 20230782PRD1.
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Publication:Watkins, S.L. SPLENDAQ: A Detector-Agnostic Data Acquisition System for Small-Scale Physics Experiments. J Low Temp Phys (2023). https://doi.org/10.1007/s10909-023-03021-w
