VASCO: A fully automated CASA pipeline for large volume VLBI data calibration

TL;DR

VASCO is an open-source fully automated pipeline that calibrates large volumes of heterogeneous VLBA archival data using CASA, significantly reducing manual effort and enabling large-scale radio astronomy data processing.

Contribution

It introduces VASCO, the first fully automated CASA-based pipeline capable of calibrating diverse VLBA data formats at scale, validated on over 1000 sources.

Findings

Achieved calibration for 97.8% of tested sources.

Reduced per-source calibration time to approximately 30 minutes.

Demonstrated feasibility of fully blind calibration of heterogeneous VLBA data.

Abstract

Calibrating large volumes of Very Long Baseline Interferometry (VLBI) data traditionally requires significant human intervention at every stage. While the Common Astronomy Software Applications (CASA) package is the standard data reduction tool across major radio observatories, no existing CASA-based pipeline operates in a fully automated manner across the heterogeneous data formats produced by the Very Long Baseline Array (VLBA) over three decades of operations. The Search for Milli-Lenses (SMILE) project, requiring the calibration of ~5000 VLBA sources, makes such blind automation a practical necessity. We introduce the VLBI and SMILE-based CASA Optimizations (VASCO) pipeline, which automates the calibration of archival VLBA data. VASCO extends the CASA-based rPICARD framework by automating preprocessing of FITS-IDI and Measurement Set data formats, calibrator and reference antenna selection via FFT-based fringe detection, and execution of the full calibration workflow. Progress tracking is handled by ALFRD (Automated Logical Framework for executing Dynamic scripts), which orchestrates pipeline execution and records results in real time. VASCO was validated on 1000 NRAO archival sources spanning 1995-2023, covering 1372 band-separated observations across the S, C, X, U, and K bands. Calibrated output was produced for 978 sources (97.8%), with 22 failures due to corrupted or incomplete data. Mean per-source execution time was ~30 minutes using MPI parallelization with up to 20 cores. VASCO demonstrates that fully blind calibration of heterogeneous archival VLBA data is achievable with CASA. The automated calibrator and reference antenna selection will be incorporated into a future rPICARD release, extending blind calibration to any supported array. VASCO and ALFRD are available as open-source Python packages.