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Constraints on the Inner Regions of Lensing Galaxies from Central Images using a Recent AGN Offset Distribution

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In gravitational lensing, central images in quads can serve as a powerful probe of the inner regions of lens galaxies. The presence of an offset central supermassive black hole (SMBH)… Click to show full abstract

In gravitational lensing, central images in quads can serve as a powerful probe of the inner regions of lens galaxies. The presence of an offset central supermassive black hole (SMBH) has the potential to distort the time-delay surface in a way such that 3 central images form: a strongly demagnified image near the SMBH, and two less demagnified (and potentially observable) images at a central maximum and saddle point. Using a quad-lens macro-model, we simulate the constraints that could be placed on various lens galaxy parameters based on their central images’ probability of detection or non-detection. Informed by a recent low-redshift distribution of off-nucleus AGN, we utilize Bayesian inference to constrain the mean SMBH off-nucleus distance and galactic core radius for a sample of 6 quads. In general, we find that a detection of the central image in any quad would favor larger SMBH off-nucleus distances and galaxy core sizes. Assuming a linear relationship between core radii and velocity dispersion rc = bσ, these results similarly imply strong constraints on b, where the likely case of a central image non-detection in each quad constraining b to $3.11^{+2.72}_{-2.26} \times 10^{-4}$ kpc km−1 s. Our results show that tight constraints on lens galaxy parameters can be made regardless of a detection or non-detection of a central image. Therefore, we recommend observational searches for the central image, possibly using our suggested novel detection technique in UV filters, to formalize stronger constraints on lens galaxy parameters.

Keywords: central images; central image; detection; inner regions; distribution

Journal Title: Monthly Notices of the Royal Astronomical Society
Year Published: 2023

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