  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.
