Lens Modeling and Cosmological Inference from an Impure Sample of Galaxy-Galaxy Strong Lenses
Abstract
The start of the Legacy Survey of Space and Time marks a new era for strong lensing science, where the number of strong lenses identified is expected to increase to 𝒪(105). In this paper we use a neural network to determine the precision with which lens parameters can be determined, using realistic simulated LSST lensed systems. We find that the Einstein radius can be measured with a mean precision of 3.7% with calibrated uncertainties accurately reflecting the corresponding measurement error. Based on the performance of current strong lens classifiers, the ∼ 100,000 detectable strong lenses are expected to be accompanied by a similar or larger number of false positives (non-lenses). In readiness for this we introduce a formalism, termed `COSMIC-BEAMS', to infer cosmological parameters while accounting for contamination by false positives. As a proof-of-concept, using simulated LSST measurements of the Einstein radii of a realistic and impure sample of photometric lens systems, i.e. those without spectroscopic confirmation, we find that the cosmological parameters Ωm, ΩΛ, and w can be measured to a precision of $0.1$, $0.03$ and $0.15$ respectively for a wCDM cosmology. We demonstrate that unbiased cosmological parameters can be inferred even in strong lens samples contaminated by 50% false positives, and that the photometric dataset of 100 000 strong lenses will provide equivalent w-precision to $2500-3500$ spectroscopic systems.
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BibTeX
@misc{holloway2026lens,
title = {{Lens Modeling and Cosmological Inference from an Impure Sample of Galaxy-Galaxy Strong Lenses}},
author = {Philip Holloway and Aprajita Verma and Philip J. Marshall and Padmavathi Venkatraman and Sydney Erickson and Tian Li and Simon Birrer and Steven Dillmann and Thomas E. Collett and the LSST Dark Energy Science Collaboration},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.21699},
eprint = {2609.21699},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.21699},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Lens Modeling and Cosmological Inference from an Impure Sample of Galaxy-Galaxy Strong Lenses
AU - Philip Holloway
AU - Aprajita Verma
AU - Philip J. Marshall
AU - Padmavathi Venkatraman
AU - Sydney Erickson
AU - Tian Li
AU - Simon Birrer
AU - Steven Dillmann
AU - Thomas E. Collett
AU - the LSST Dark Energy Science Collaboration
PY - 2026
DA - 2026/09/18
PB - arXiv
DO - 10.48550/arxiv.2609.21699
UR - https://arxiv.org/abs/2609.21699
AB - The start of the Legacy Survey of Space and Time marks a new era for strong lensing science, where the number of strong lenses identified is expected to increase to 𝒪(10^5). In this paper we use a neural network to determine the precision with which lens parameters can be determined, using realistic simulated LSST lensed systems. We find that the Einstein radius can be measured with a mean precision of 3.7% with calibrated uncertainties accurately reflecting the corresponding measurement error. Based on the performance of current strong lens classifiers, the ∼ 100,000 detectable strong lenses are expected to be accompanied by a similar or larger number of false positives (non-lenses). In readiness for this we introduce a formalism, termed `COSMIC-BEAMS', to infer cosmological parameters while accounting for contamination by false positives. As a proof-of-concept, using simulated LSST measurements of the Einstein radii of a realistic and impure sample of photometric lens systems, i.e. those without spectroscopic confirmation, we find that the cosmological parameters Ω_m, Ω_Λ, and w can be measured to a precision of $0.1$, $0.03$ and $0.15$ respectively for a wCDM cosmology. We demonstrate that unbiased cosmological parameters can be inferred even in strong lens samples contaminated by 50% false positives, and that the photometric dataset of 100 000 strong lenses will provide equivalent w-precision to $2500-3500$ spectroscopic systems.
ER -