Shedding Light on Low Surface Brightness Galaxies in Dark Energy Survey with Transformers

Low surface brightness galaxies (LSBGs) which are defined as galaxies that are fainter than the night sky, play a crucial role in understanding galaxy evolution and cosmological models. Upcoming large-scale surveys like Rubin Observatory Legacy Survey of Space and Time (LSST) and Euclid are expected to observe billions of astronomical objects. In this context, using semi-automatic methods to identify LSBGs would be a highly challenging and time-consuming process and demand automated or machine learning-based methods to overcome this challenge. We study the use of transformer models in separating LSBGs from artefacts in the data from the Dark Energy Survey (DES) data release 1. Using the transformer models, we then search for new LSBGs from the DES that the previous searches may have missed. Properties of the newly found LSBGs are investigated, along with an analysis of the properties of the total LSBG sample in DES. We identified 4,083 new LSBGs in DES, adding an additional $\sim17\% $ to the LSBGs already known in DES. This also increased the number density of LSBGs in DES to 5.5 deg$^{-2}$. We performed a clustering analysis of the LSBGs in DES using an angular two-point auto-correlation function and found that LSBGs cluster more strongly than their high surface brightness counterparts. We associated 1310 LSBGs with galaxy clusters and identified 317 among them as ultra-diffuse galaxies (UDGs). We found that these cluster LSBGs are getting bluer and larger in size towards the edge of the clusters when compared with those in the centre. Transformer models have the potential to be on par with convolutional neural networks as state-of-the-art algorithms in analysing astronomical data.