Kavli Affiliate: Aaron Roodman
| First 5 Authors: Padmavathi Venkatraman, Padmavathi Venkatraman, , ,
| Summary:
Strong gravitational lensing of active galactic nuclei (AGN) enables
measurements of cosmological parameters through time-delay cosmography (TDC).
With data from the upcoming LSST survey, we anticipate using a sample of
O(1000) lensed AGN for TDC. To prepare for this dataset and enable this
measurement, we construct and analyze a realistic mock sample of 1300 systems
drawn from the OM10 (Oguri & Marshall 2010) catalog of simulated lenses with
AGN sources at $z<3.1$ in order to test a key aspect of the analysis pipeline,
that of the lens modeling. We realize the lenses as power law elliptical mass
distributions and simulate 5-year LSST i-band coadd images. From every image,
we infer the lens mass model parameters using neural posterior estimation
(NPE). Focusing on the key model parameters, $theta_E$ (the Einstein Radius)
and $gamma_lens$ (the projected mass density profile slope), with consistent
mass-light ellipticity correlations in test and training data, we recover
$theta_E$ with less than 1% bias per lens, 6.5% precision per lens and
$gamma_lens$ with less than 3% bias per lens, 8% precision per lens. We find
that lens light subtraction prior to modeling is only useful when applied to
data sampled from the training prior. If emulated deconvolution is applied to
the data prior to modeling, precision improves across all parameters by a
factor of 2. Finally, we combine the inferred lens mass models using Bayesian
Hierarchical Inference to recover the global properties of the lens sample with
less than 1% bias.
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