Present-day multi-wavelength deep imaging surveys allow to characterise the outskirts of galaxies with unprecedented precision. Taking advantage of this situation, we define a new physically motivated measurement of size for galaxies based on the expected location of the gas density threshold for star formation. Employing both theoretical and observational arguments, we use the stellar mass density contour at 1 M pc −2 as a proxy for this density threshold for star formation. This choice makes our size definition operative. With this new size measure, the intrinsic scatter of the global stellar mass (M ) -size relation (explored over five orders of magnitude in stellar mass) decreases to ∼0.06 dex. This value is 2.5 times smaller than the scatter measured using the effective radius (∼0.15 dex) and between 1.5 and 1.8 times smaller than those using other traditional size indicators such as R 23.5,i (∼0.09 dex), the Holmberg radius R H (∼0.09 dex) and the half-mass radius R e,M (∼0.11 dex). Moreover, galaxies with 10 7 M < M < 10 11 M increase monotonically in size following a power-law with a slope very close to 1/3, equivalent to an average stellar mass 3D density of ∼4.5×10 −3 M pc −3 for galaxies within this mass range. Galaxies with M >10 11 M show a different slope with stellar mass, which is suggestive of a larger gas density threshold for star formation at the epoch when their star formation peaks.
Now almost 70 years since its introduction, the effective or half-light radius has become a very popular choice for characterising galaxy size. However, the effective radius measures the concentration of light within galaxies and thus does not capture the intuitive definition of size which is related to the edge or boundary of objects. For this reason, we aim to demonstrate the undesirable consequence of using the effective radius to draw conclusions about the nature of faint 'ultra-diffuse galaxies' (UDGs) when compared to dwarfs and Milky Way-like galaxies. Instead of the effective radius, we use a measure of galaxy size based on the location of the gas density threshold required for star formation. Compared to the effective radius, this physically motivated definition places the sizes much closer to the boundary of a galaxy. Therefore, considering the sizes and stellar mass density profiles of UDGs and regular dwarfs, we find that the UDGs have sizes that are within the size range of dwarfs. We also show that currently known UDGs do not have sizes comparable to Milky Way-like objects. We find that, on average, UDGs are ten times smaller in extension than Milky Way-like galaxies. These results show that the use of size estimators sensitive to the concentration of light can lead to misleading results.
Context. With the growth of the scale, depth, and resolution of astronomical imaging surveys, there is increased need for highly accurate automated detection and extraction of astronomical sources from images. This also means there is a need for objective quality criteria, and automated methods to optimise parameter settings for these software tools. Aims. We present a comparison of several tools developed to perform this task: namely SExtractor, ProFound, NoiseChisel, and MTObjects. In particular, we focus on evaluating performance in situations that present challenges for detection. For example, faint and diffuse galaxies; extended structures, such as streams; and objects close to bright sources. Furthermore, we develop an automated method to optimise the parameters for the above tools. Methods. We present four different objective segmentation quality measures, based on precision, recall, and a new measure for the correctly identified area of sources. Bayesian optimisation is used to find optimal parameter settings for each of the four tools when applied to simulated data, for which a ground truth is known. After training, the tools are tested on similar simulated data in order to provide a performance baseline. We then qualitatively assess tool performance on real astronomical images from two different surveys. Results. We determine that when area is disregarded, all four tools are capable of broadly similar levels of detection completeness, while only NoiseChisel and MTObjects are capable of locating the faint outskirts of objects. MTObjects achieves the highest scores on all tests for all four quality measures, whilst SExtractor obtains the highest speeds. No tool has sufficient speed and accuracy to be well suited to large-scale automated segmentation in its current form.
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