Statistical strong lensing

Sonnenfeld, Alessandro

doi:10.1051/0004-6361/202142301

Cited by 15 publications

(7 citation statements)

References 41 publications

(37 reference statements)

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“…In order to do so, it is useful to introduce the concept of strong lensing cross-section. Given a foreground galaxy with parameters ψ g , a background source with parameters ψ s , and a criterion S to define a strong lensing event, the strong lensing cross-section is defined as (Sonnenfeld 2022):…”

Section: Individual Lensesmentioning

confidence: 99%

“…If one wishes to directly account for the strong lensing bias, the formally correct procedure is to explicitly model all of the selection steps in a Bayesian hierarchical formalism, as explained by Sonnenfeld (2022). Although this can be computationally challenging, machine learning can offer an efficient alternative (Legin et al 2022).…”

Section: Mitigation Strategiesmentioning

confidence: 99%

“…In general, the probability distribution P SL of a sample of strong lenses from a given survey with selection criterion S is given by (Sonnenfeld 2022): P SL (ψ g , ψ s |S ) ∝ P g (ψ g )P s (ψ s )P sel (ψ g , ψ s |S ).…”

Section: Introductionmentioning

confidence: 99%

“…In order to obtain an unbiased estimate of either P g or P s , then, it is necessary to invert Equation 1. In principle, this can be done with a Bayesian hierarchical formalism (Sonnenfeld 2022), but knowledge of the lens selection probability P sel is required. This factor can be written as the following product: P sel (ψ g , ψ s |S ) = P det (ψ g , ψ s )P find (ψ g , ψ s |S ),…”

Section: Introductionmentioning

confidence: 99%

“…Sonnenfeld (2022) implicitly assumed P find ≡ 1, therefore P det and P sel can be used interchangeably in the context of that work.…”

mentioning

confidence: 99%

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Strong lensing selection effects

Sonnenfeld¹,

Li²,

Despali³

et al. 2023

Preprint

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Context. Strong lenses are a biased subset of the general population of galaxies. Aims. The goal of this work is to quantify how lens galaxies and lensed sources differ from their parent distribution, namely the strong lensing bias. Methods. We first studied how the strong lensing cross-section varies as a function of lens and source properties. Then, we simulated strong lensing surveys with data similar to that expected for Euclid and measured the strong lensing bias in different scenarios. We focused particularly on two quantities: the stellar population synthesis mismatch parameter, α sps , defined as the ratio between the true stellar mass of a galaxy and the stellar mass obtained from photometry, and the central dark matter mass at fixed stellar mass and size. Results. Strong lens galaxies are biased towards larger stellar masses, smaller half-mass radii and larger dark matter masses. The amplitude of the bias depends on the intrinsic scatter in the mass-related parameters of the galaxy population and on the completeness in Einstein radius of the lens sample. For values of the scatter that are consistent with observed scaling relations and a minimum detectable Einstein radius of 0.5 , the strong lensing bias in α sps is 10%, while that in the central dark matter mass is 5%. The bias has little dependence on the properties of the source population: samples of galaxy-galaxy lenses and galaxy-quasar lenses that probe the same Einstein radius distribution are biased in a very similar way. Quadruply imaged quasar lenses, however, are biased towards higher ellipticity galaxies. Conclusions. Given current uncertainties, strong lensing observations can be used directly to improve our current knowledge of the inner structure of galaxies, without the need to correct for selection effects.

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Section: Individual Lensesmentioning

confidence: 99%

Section: Mitigation Strategiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Sonnenfeld (2022) implicitly assumed P find ≡ 1, therefore P det and P sel can be used interchangeably in the context of that work.…”

mentioning

confidence: 99%

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Strong lensing selection effects

Sonnenfeld¹,

Li²,

Despali³

et al. 2023

Preprint

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Strong lensing time-delay cosmography in the 2020s

Treu

Suyu

Marshall

2022

Astron Astrophys Rev

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Multiply imaged time-variable sources can be used to measure absolute distances as a function of redshifts and thus determine cosmological parameters, chiefly the Hubble Constant H$$_0$$ 0 . In the two decades up to 2020, through a number of observational and conceptual breakthroughs, this so-called time-delay cosmography has reached a precision sufficient to be an important independent voice in the current “Hubble tension” debate between early- and late-universe determinations of H$$_0$$ 0 . The 2020s promise to deliver major advances in time-delay cosmography, owing to the large number of lenses to be discovered by new and upcoming surveys and the vastly improved capabilities for follow-up and analysis. In this review, after a brief summary of the foundations of the method and recent advances, we outline the opportunities for the decade and the challenges that will need to be overcome in order to meet the goal of the determination of H$$_0$$ 0 from time-delay cosmography with 1% precision and accuracy.

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Strong Gravitational Lensing as a Probe of Dark Matter

Vegetti,

Birrer,

Despali

et al. 2024

Space Sci Rev

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Dark matter structures within strong gravitational lens galaxies and along their lines of sight leave a gravitational imprint on the multiple images of lensed sources. Strong gravitational lensing provides, therefore, a key test of different dark matter models. In this article, we describe how galaxy-scale strong gravitational lensing observations are sensitive to the physical nature of dark matter. We provide an historical perspective of the field, and review its current status. We discuss the challenges and advances in terms of data, treatment of systematic errors and theoretical predictions, that will enable one to deliver a stringent and robust test of different dark matter models in the next decade. With the advent of the next generation of sky surveys, the number of known strong gravitational lens systems is expected to increase by several orders of magnitude. Coupled with high-resolution follow-up observations, these data will provide a key opportunity to constrain the properties of dark matter with strong gravitational lensing.

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Statistical strong lensing

Cited by 15 publications

References 41 publications

Strong lensing selection effects

Strong lensing selection effects

Strong lensing time-delay cosmography in the 2020s

Strong Gravitational Lensing as a Probe of Dark Matter

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