Developing a Victorious Strategy to the Second Strong Gravitational
  Lensing Data Challenge
  Strong Lensing is a powerful probe of the matter distribution in galaxies and
clusters and a relevant tool for cosmography. Analyses of strong gravitational
lenses with Deep Learning have become a popular approach due to these
astronomical objects' rarity and image complexity. Next-generation surveys will
provide more opportunities to derive science from these objects and an
increasing data volume to be analyzed. However, finding strong lenses is
challenging, as their number densities are orders of magnitude below those of
galaxies. Therefore, specific Strong Lensing search algorithms are required to
discover the highest number of systems possible with high purity and low false
alarm rate. The need for better algorithms has prompted the development of an
open community data science competition named Strong Gravitational Lensing
Challenge (SGLC). This work presents the Deep Learning strategies and
methodology used to design the highest-scoring algorithm in the II SGLC. We
discuss the approach used for this dataset, the choice for a suitable
architecture, particularly the use of a network with two branches to work with
images in different resolutions, and its optimization. We also discuss the
detectability limit, the lessons learned, and prospects for defining a
tailor-made architecture in a survey in contrast to a general one. Finally, we
release the models and discuss the best choice to easily adapt the model to a
dataset representing a survey with a different instrument. This work helps to
take a step towards efficient, adaptable and accurate analyses of strong lenses
with deep learning frameworks.
