  Over the last two decades, around 300 quasars have been discovered at
$z\gtrsim6$, yet only one has identified as being strongly gravitationally
lensed. We explore a new approach -- enlarging the permitted spectral parameter
space, while introducing a new spatial geometry veto criterion -- which is
implemented via image-based deep learning. We first apply this approach to a
systematic search for reionization-era lensed quasars, using data from the Dark
Energy Survey, the Visible and Infrared Survey Telescope for Astronomy
Hemisphere Survey, and the Wide-field Infrared Survey Explorer.Our search
method consists of two main parts: (i) the preselection of the candidates based
on their spectral energy distributions (SEDs) using catalog-level photometry
and (ii) relative probabilities calculation of the candidates being a lens or
some contaminant, utilizing a convolutional neural network (CNN)
classification. The training data sets are constructed by painting deflected
point-source lights over actual galaxy images, to generate realistic
galaxy-quasar lens models, optimized to find systems with small image
separations, i.e., Einstein radii of $\theta_\mathrm{E} \leq 1$ arcsec. Visual
inspection is then performed for sources with CNN scores of $P_\mathrm{lens} >
0.1$, which leads us to obtain 36 newly selected lens candidates, which are
awaiting spectroscopic confirmation. These findings show that automated SED
modeling and deep learning pipelines, supported by modest human input, are a
promising route for detecting strong lenses from large catalogs that can
overcome the veto limitations of primarily dropout-based SED selection
approaches.
