Kavli Affiliate: Aaron Roodman
| First 5 Authors: Sydney Erickson, Sebastian Wagner-Carena, Phil Marshall, Martin Millon, Simon Birrer
| Summary:
Strongly lensed quasars can be used to constrain cosmological parameters
through time-delay cosmography. Models of the lens masses are a necessary
component of this analysis. To enable time-delay cosmography from a sample of
$mathcal{O}(10^3)$ lenses, which will soon become available from surveys like
the Rubin Observatory’s Legacy Survey of Space and Time (LSST) and the Euclid
Wide Survey, we require fast and standardizable modeling techniques. To address
this need, we apply neural posterior estimation (NPE) for modeling galaxy-scale
strongly lensed quasars from the Strong Lensing Insights into the Dark Energy
Survey (STRIDES) sample. NPE brings two advantages: speed and the ability to
implicitly marginalize over nuisance parameters. We extend this method by
employing sequential NPE to increase precision of mass model posteriors. We
then fold individual lens models into a hierarchical Bayesian inference to
recover the population distribution of lens mass parameters, accounting for
out-of-distribution shift. After verifying our method using simulated analogs
of the STRIDES lens sample, we apply our method to 14 Hubble Space Telescope
single-filter observations. We find the population mean of the power-law
elliptical mass distribution slope, $gamma_{text{lens}}$, to be
$mathcal{M}_{gamma_{text{lens}}}=2.13 pm 0.06$. Our result represents the
first population-level constraint for these systems. This population-level
inference from fully automated modeling is an important stepping stone towards
cosmological inference with large samples of strongly lensed quasars.
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