Our understanding of galaxy formation, evolution, and the relationship between galaxies and the dark matter halos that they inhabit is constrained by our ability to detect faint galaxies. Low-surface-brightness galaxies (LSBGs) are observationally defined as galaxies with central surface brightness fainter than the night sky – thus, by definition, they are objects difficult to detect and study, especially across a wide sky area and different environments. In this talk, I will present the search for LSBGs in the Dark Energy Survey (DES), its challenges, and the resulting galaxy catalog - the largest such catalog to date.
In the second part of the talk I will discuss the development of automated, deep learning-based, pipelines for LSBG detection (separation of LSB galaxies from LSB artefacts present in images) and morphological classification. Such techniques will be extremely valuable in the advent of very large future surveys like the planned Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory.