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Lens Model Accuracy in the Expected LSST Lensed AGN Sample
Cosmology and Nongalactic Astrophysics · Submitted 23 Oct 2025 · Published version
abstract compiled · 2026-09-24 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Strong gravitational lensing of active galactic nuclei (AGN) enables measurements of cosmological parameters through time-delay cosmography (TDC). | background 1.00 | 0.05 | 0.03 | asserted 0.96 |
| s1 | With data from the upcoming LSST survey, we anticipate using a sample of O(1000) lensed AGN for TDC. | background 0.87 | 0.20 | 0.03 | hedged possibility 0.81 |
| s2 | 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. | method 0.52 | 0.93 | 0.04 | asserted 0.99 |
| s3 | We realize the lenses as power law elliptical mass distributions and simulate 5-year LSST i-band coadd images. | method 1.00 | 0.93 | 0.02 | asserted 0.99 |
| s4 | From every image, we infer the lens mass model parameters using neural posterior estimation (NPE). | method 1.00 | 0.95 | 0.03 | asserted 0.99 |
| s5 | 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. | result 1.00 | 0.96 | 0.43 | asserted 1.00 |
| s6 | We find that lens light subtraction prior to modeling is only useful when applied to data sampled from the training prior. | result 0.99 | 0.95 | 0.09 | asserted 1.00 |
| s7 | If emulated deconvolution is applied to the data prior to modeling, precision improves across all parameters by a factor of 2. | result 1.00 | 0.91 | 0.11 | asserted 1.00 |
| s8 | 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. | result 0.97 | 0.96 | 0.25 | asserted 1.00 |
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Original abstract
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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NPE guides the CNN output features to approximately describe a posterior PDF for the parameters listed above – an input image $d_k$, is mapped to an output $q_ϕ(ξ_k | d_k)$, where $ϕ$ represents the model weights [ref] .
Fast $\epsilon$-free Inference of Simulation Models with Bayesian Conditional Density Estimation · Individual Lens Mass Models: Neural Posterior Estimation · 0.96
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