When Spectral Modeling Meets Convolutional Networks: A Method for Discovering Reionization-era Lensed Quasars in Multi-band Imaging Data by Irham Taufik Andika et al. on Tuesday 29 November

Over the last two decades, around three hundred quasars have been discovered

at $z\gtrsim6$, yet only one was identified as being strong-gravitationally

lensed. We explore a new approach, enlarging the permitted spectral parameter

space while introducing a new spatial geometry veto criterion, implemented via

image-based deep learning. We made the first application of this approach in 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) pre-selection of the candidates based on

their spectral energy distributions (SEDs) using catalog-level photometry and

(ii) relative probabilities calculation of being a lens or some contaminant

utilizing a convolutional neural network (CNN) classification. The training

datasets 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 led us to obtain 36

newly-selected lens candidates, waiting for 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.

arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2211.14543v1