SIMBAD references

Context. Very long baseline interferometry (VLBI) is a powerful observational technique that can achieve sub-milliarcsecond resolution. However, it requires complex and often manual post-correlation calibration to correct for instrumental, geometric, and propagation-related errors. Unlike connected-element interferometers, VLBI arrays typically deliver raw visibilities rather than science-ready data, and existing pipelines are largely semi-automated and reliant on user supervision.
Aims. We aim to develop and validate a fully automated, end-to-end calibration pipeline for continuum VLBI data that operates without human intervention or prior knowledge of the dataset. The pipeline must be scalable to thousands of sources and suitable for heterogeneous archival observations, as required by initiatives such as the Search for Milli-Lenses (SMILE) project.
Methods. We present the VLBI Pipeline for automated data Calibration using AIPS, or VIPCALs. Implemented in Python using ParselTongue, VIPCALs reproduces the standard AIPS calibration workflow in a fully unsupervised mode. The pipeline carries out data import, retrieval of system temperature and gain curve data, ionospheric and geometric corrections, fringe fitting, and amplitude and bandpass calibration steps. VIPCALs performs automatic reference antenna selection and calibrator identification, and it generates diagnostic outputs for inspection. It can be easily used through a simple graphical user interface. We validated VIPCALs on a representative sample of Very Long Baseline Array (VLBA) data corresponding to 1000 sources from the SMILE project.
Results. VIPCALs successfully calibrated observations of 955 of the 1000 test sources across multiple frequency bands. Over 91% of the calibrated datasets achieved successful fringe fitting on target in at least half of the solutions attempted. The median ratio of calibrated visibilities to initial total visibilities was 0.87. The average processing time was below 10 minutes per dataset when using a single-core configuration, demonstrating both efficiency and scalability.
Conclusions. VIPCALs enables robust, reproducible, and fully automated calibration of VLBI continuum data, significantly lowering the entry barrier for VLBI science and making large-scale projects such as SMILE feasible.