Context. Constraining the properties, spatial distribution, and luminosity function of dwarf galaxies in different galactic environments is crucial for understanding the dwarf galaxy formation and evolution. Large surveys such as the Kilo Degree Survey (KiDS) provide useful publicly available datasets that can be used to identify dwarf galaxy candidates in a range of galactic neighborhoods. The resulting catalogs are useful for constraining the abundance of dwarfs in different environments and also provide useful galaxy samples for future follow-up studies. Ultimately this analysis of low-mass galaxies also provides constraints on our cosmological galaxy formation models. Aims. We generated a dwarf galaxy candidate catalog based on the KiDS images. KiDS data covers a 1004 deg2 area in u′, g′, r′, and i′ filters that is centered on two horizontal stripes at the equator and in the southern hemisphere. In our catalog we provide the locations, photometric properties, and visual classifications of dwarf galaxy candidates within 60 Mpc in all different environments covered by the KiDS. We also use the catalog to analyze the dwarf galaxy numbers and distributions in groups as a function of groups’ virial mass. Methods. We used Max-Tree Objects (MTO) to identify sources from the KiDS data. We then selected objects based on their detection sizes and surface brightness. We used automated photometric pipeline to run GALFIT on the images in order to measure the structure, brightness, and color of the objects. We then used size, surface brightness, and color cuts to exclude the likely background galaxies and classify the likelihoods of the remaining objects being dwarf galaxies based on their visual appearance. We also probed the completeness limits and detection biases of our detection procedure, by embedding simulated galaxies into the KiDS images. Results. Our catalog contains galaxies that have Re larger than 3 arcsec and reaches the 50% completeness limit at the r′-band mean effective surface brightness of 26 mag arcsec−2. Near the completeness limit there is a slight selection bias toward detecting more round and centrally peaked objects more effectively than the more elongated and centrally flat. Altogether we identified 4 × 107 objects from the KiDs data. After applying the size, color, and surface brightness cuts, we were left with 6230 objects for which we performed photometry and visual classifications. We ranked those objects into five classes based on their likeliness of being a dwarf. We identified 763 galaxies as clear dwarfs, 793 as likely dwarfs, and 933 as possible dwarfs. The remaining objects are likely not dwarfs. Based on the distances of groups that the dwarfs are likely to be associated with, the dwarfs are expected to lie at distances of between 14 Mpc −60 Mpc. The majority of dwarfs in the sample have magnitudes of between 14 mag < mr <20 mag, effective radii of between 1 arcsec < Re <30 arcsec, and mean effective surface brightnesses of between 21 mag arcsec−2 < μ̄r,e < 25 mag arcsec−2. We compare the measured properties of the galaxies in our catalog with values from the literature and find mostly good agreement between those, when considering the differences in the data qualities. The only exceptions are the effective radii, which are systematically smaller in our catalog, due to the background subtraction method used in the KiDS data reduction. We also identify the most likely associations with groups and cluster for all the dwarfs in our catalog. Additionally we compare the number of dwarfs and their distribution within the groups with similar dwarfs found in the Illustris-TNG simulations. We find no statistically significant tension between the dwarf numbers and distributions between the observations and the simulations. Conclusions. Our catalog contains locations, colors, structural parameters, and likely group memberships for 2489 dwarf galaxy candidates. All the measurements are publicly available. The catalog can be used to study properties of dwarfs in a range of environments and it provides a good dataset for follow-up studies.
Venhola et al. (Tue,) studied this question.