Strax

strax is an open-source data processing framework designed for high-throughput analysis of detector data in particle and astroparticle physics. It originated within the XENON collaboration to support the analysis of data from liquid xenon time projection chambers and has since been used in other experiments. The core idea is to organize analysis as a set of small, reusable processors that declare their input and output data types, allowing users to build end-to-end pipelines that transform raw waveforms into higher-level quantities such as reconstructed signals and events.

Key features include modularity, declarative pipelines, and data provenance. Each processor consumes one or more data types and produces new ones; strax automatically handles execution order, caching, and reproducibility by recording configurations and versions. This enables researchers to rerun analyses with different parameters without recomputing unchanged results. Strax also supports parallel execution and scalable handling of large datasets.

Implementation and ecosystem: Strax is implemented in Python and relies on common scientific libraries for array operations and I/O. It uses a registry of data types and processors and provides tooling to inspect, configure, and document data flows. The strax ecosystem includes extensions such as straxen, which provides detector-specific data types and processing steps for XENON experiments. Strax is open source with documentation and community support via its repository and mailing lists.

Impact and purpose: By providing a standardized framework for data processing, Strax helps ensure reproducibility across analyses and experiments, facilitating collaboration and long-term data preservation. It is associated with XENON and related detector work and remains a reference tool in its niche.