KiDS DR5 low-surface-brightness galaxies

We used an ensemble consisting of one convolutional neural network (CNN) and two transformer models trained on DES cutouts and applied to KiDS DR5 imaging with surface-brightness normalisation. Structural parameters were estimated with galfitm, and the sample was further refined through visual inspection to produce the final candidate sample. Photometric redshift and stellar population properties were estimated through spectral energy distribution (SED) fitting with CIGALE.

We identify 20180 LSBGs and 434 Ultra-diffuse galaxies (UDGs) in KiDS DR5. Their structural parameters are similar to the known LSBGs from DES and Hyper Suprime-Cam SSP Survey (HSC-SSP). The KiDS-LSBGs follow a continuous size-luminosity relation connecting classical dwarf galaxies and UDGs, and their colours are bimodal (~73% blue, ~27% red).

Cross-matching with spectroscopic and cluster catalogues provides redshifts for 4 913 systems, enabling a systematic characterisation of the star-forming main sequence of LSBGs. Photometric redshifts derived via SED fitting are mildly overestimated (=~0.024), leading to systematic offsets in stellar mass and star formation rate estimates. However, these biases induce only small shifts (~0.13-0.22dex) in specific star formation rate, thereby preserving the structure of the star-forming main sequence.

Strong environmental trends are also evident, with cluster LSBGs and UDGs exhibiting redder colours and reduced star formation compared to non-cluster systems. This indicates efficient quenching driven by environmental processes. We demonstrate that, with domain adaptation, cross-survey LSBG identification can be achieved with deep learning models.

Thus, the methodology presented here provides a powerful and scalable pathway for constructing homogeneous LSBG catalogues across surveys. This framework is well-suited for the era of LSST and Euclid, where millions of diffuse galaxies will be discovered, and consistent cross-survey classification will become essential.