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

TL;DR

SPLENDAQ is a versatile, detector-agnostic Python-based data acquisition system designed for small-scale physics experiments, enabling efficient offline analysis with threshold-triggering and noise measurement algorithms.

Contribution

The paper introduces SPLENDAQ, a novel detector-agnostic DAQ package that simplifies data processing for diverse physics experiments using minimal setup.

Findings

Effective threshold-triggering based on optimal filter formalism

Capability to select random nonoverlapping segments for noise analysis

Integration with Moku platform for streamlined event building

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.