Efficient and Versatile Toolbox for Analysis of Time-Tagged Measurements
Zuzeng Lin, Lucas Schweickert, Samuel Gyger, Klaus D. J\"ons, Val, Zwiller
TL;DR
The paper introduces ETA, a versatile software tool that efficiently analyzes large, complex time-tagged data, surpassing traditional methods in flexibility and computational performance.
Contribution
We present ETA, a novel software platform enabling flexible, efficient analysis of time-tagged data with customizable correlation extraction and high computational performance.
Findings
ETA handles increasing data complexity and volume effectively.
The tool allows for analysis beyond traditional start-stop measurements.
ETA maintains high computational efficiency with flexible analysis options.
Abstract
Acquisition and analysis of time-tagged events is a ubiquitous tool in scientific and industrial applications. With increasing time resolution, number of input channels, and acquired events, the amount of data can be overwhelming for standard processing techniques. We developed the Extensible Time-tag Analyzer (ETA), a powerful and versatile, yet easy to use software to efficiently analyze and display time-tagged data. Our tool allows for flexible extraction of correlation from time-tagged data beyond start-stop measurements that were traditionally used. A combination of state diagrams and simple code snippets allows for analysis of arbitrary complexity while keeping computational efficiency high.
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