A variety of MeerKAT users have developed their own analysis pipelines. We list the main ones known to us below. Any questions should be addressed to their authors.

CARACal

https://ui.adsabs.harvard.edu/abs/2020ascl.soft06014J/abstract

CARACal stands for Containerized Automated Radio Astronomy Calibration. CARACal can reduce large data sets and produce high-dynamic-range continuum images and spectroscopic data cubes. The pipeline is platform-independent and delivers imaging quality metrics to efficiently assess the data quality.

Installation and usage instructions can be found here.

oxkat

https://ui.adsabs.harvard.edu/abs/2020ascl.soft09003H/abstract

oxkat semi-automatically performs calibration and imaging of data from the MeerKAT radio telescope. Taking as input raw visibilities in Measurement Set format, the entire processing workflow is covered, from flagging and reference calibration, to imaging and self-calibration, and (optionally) direction-dependent calibration.

Installation and usage instructions can be found here.

ARTIP

https://ui.adsabs.harvard.edu/abs/2018ascl.soft02004S/abstract

The Automated Radio Telescope Image Processing Pipeline (ARTIP) automates the entire process of flagging, calibrating, and imaging for radio-interferometric data.

Installation and usage instructions can be found here.

IDIA pipeline

https://idia-pipelines.github.io

The IDIA Pipeline is a CASA based pipeline that does calibration (cross- and self-cal) and imaging of MeerKAT datasets. The pipeline is optimised to run on IDIA’s SLURM cluster, but has been demonstrated to work on PBS-based systems. The software uses Multi Measurement Sets (MMSs) and CASA’s MPICASA to distribute commonly used tasks across several nodes (e.g., flagging and imaging), which dramatically reduces the typical runtime of a calibration and imaging run.