When Spectral Modeling Meets Convolutional Networks: A Method for Discovering Reionization-era Lensed Quasars in Multi-band Imaging Data

• URL: http://arxiv.org/abs/2211.14543v1
• Date: Sat, 26 Nov 2022 11:27:13 GMT
• Title: When Spectral Modeling Meets Convolutional Networks: A Method for Discovering Reionization-era Lensed Quasars in Multi-band Imaging Data
• Authors: Irham Taufik Andika, Knud Jahnke, Arjen van der Wel, Eduardo Ba\~nados, Sarah E. I. Bosman, Frederick B. Davies, Anna-Christina Eilers, Anton Timur Jaelani, Chiara Mazzucchelli, Masafusa Onoue, and Jan-Torge Schindler
• Abstract summary: We introduce a new spatial geometry veto criterion, implemented via image-based deep learning. We make the first application of this approach in a systematic search for reionization-era lensed quasars. The training datasets are constructed by painting deflected point-source lights over actual galaxy images to generate realistic galaxy-quasar lens models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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