Generating images from radio interferometer data requires deconvolving the point spread function of the array from the initial image. This process is commonly done via the clean algorithm, which iteratively models the observed emission. Because this algorithm has many degrees of freedom, producing an optimal science image typically requires the scientist to manually mask regions of real emission while cleaning. This process is a major hurdle for the creation of the automated imaging pipelines necessary to process the high data rates produced by current and future interferometers like ALMA, the JVLA, and the ngVLA. In this talk, we present a general purpose masking algorithm called ‘auto-multithresh’ that automatically masks emission during the cleaning process. This algorithm was initially implemented within the tclean task in CASA 5.1. The tclean implementation significant performance improvements in CASA 5.3. The ‘auto-multithresh’ algorithm is in production as part of the ALMA Cycle 5 and 6 imaging pipelines. It has also been shown to work with data from telescopes like the VLA and ATCA. We describe how this algorithm works, provide a variety of examples demonstrating a success of the algorithm, and discuss the performance of the algorithm. Finally, we close with some future directions for producing science ready data products that build on this algorithm.
Link to PDF (may not be available yet): O12-1.pdf