Speaker
Description
Very long baseline interferometry (VLBI) requires complex and often manual post-correlation calibration to correct for instrumental, geometric, and propagation-related errors. Unlike connected-element interferometers, VLBI arrays typically provide raw visibilities rather than science-ready data, and existing pipelines are largely semi-automated and reliant on user supervision. This manual oversight becomes a critical bottleneck for large-scale VLBI projects involving thousands of radio sources.
We present VIPCALs, a fully automated, end-to-end calibration pipeline for continuum VLBI data that operates without human intervention or prior knowledge of the dataset. Designed for scalability to thousands of sources and heterogeneous archival observations, VIPCALs addresses the needs of initiatives such as the Search for Milli-Lenses (SMILE) project. Implemented in AIPS using ParselTongue, VIPCALs reproduces the standard calibration workflow in a fully unsupervised mode. Besides the usual calibration tasks, the pipeline also performs data preprocessing, automatic reference antenna selection, calibrator identification, and generates diagnostic outputs for inspection. The pipeline has been validated on a representative sample of Very Long Baseline Array (VLBA) data covering 1000 sources from the SMILE project. The results demonstrate that fully automated VLBI calibration is feasible, paving the way for incoming large-scale VLBI projects.
