BLOBCAT: Software to Catalogue Flood-Filled Blobs in Radio Images of Total Intensity and Linear Polarization
Christopher A. Hales, Tara Murphy, James R. Curran, Enno Middelberg,, Bryan M. Gaensler, Ray P. Norris

TL;DR
BLOBCAT is a new software tool that uses flood fill algorithms to detect and accurately catalogue sources in radio astronomical images, especially effective for large survey data in total intensity and linear polarization.
Contribution
The paper introduces BLOBCAT, a novel source extraction software with automated noise estimation and bias corrections, optimized for large radio survey datasets.
Findings
BLOBCAT accurately measures flux densities and positions in simulated data.
It performs well in total intensity and linear polarization measurements.
Benchmarking shows superior performance over standard Gaussian fitting methods.
Abstract
We present BLOBCAT, new source extraction software that utilises the flood fill algorithm to detect and catalogue blobs, or islands of pixels representing sources, in two-dimensional 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 byproduct 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…
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