blobcat: software to catalogue flood-filled blobs in radio images of total intensity and linear polarization

We present blobcat, new source extraction software that utilizes the flood fill algorithm to detect and catalogue blobs, or islands of pixels representing sources, in 2D astronomical images. The software is designed to process radio-wavelength images of both Stokes I intensity and linear polarization, the latter formed through the quadrature sum of Stokes Q and U intensities or as a by-product of rotation measure synthesis. We discuss an objective, automated method by which estimates of position-dependent background root mean square noise may be obtained and incorporated into blobcat's analysis. We derive and implement within blobcat corrections for two systematic biases to enable the flood fill algorithm to accurately measure flux densities for Gaussian sources. We discuss the treatment of non-Gaussian sources in light of these corrections. We perform simulations to validate the flux density and positional measurement performance of blobcat, and we benchmark the results against those of a standard Gaussian fitting task. We demonstrate that blobcat exhibits accurate measurement performance in total intensity and, in particular, linear polarization. blobcat is particularly suited to the analysis of large survey data.