SLITRONOMY: Towards a fully wavelet-based strong lensing inversion technique

Galan, A.; Peel, Austin; Joseph, R.; Courbin, F.; Starck, Jean-Luc

doi:10.1051/0004-6361/202039363

Cited by 28 publications

(24 citation statements)

References 80 publications

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“…Birrer et al 2016Birrer et al , 2019. Most of the time, the source is either pixelised with regularisation applied (Brewer & Lewis 2008;Suyu & Halkola 2010;Nightingale & Dye 2015;Galan et al 2021) or analytical with a main simple profile plus a set of basis functions, such as shapelets, that are designed to capture morphological host complexity beyond axisymmetry (Birrer et al 2015(Birrer et al , 2016(Birrer et al , 2019Birrer & Amara 2018;Shajib et al 2019). Above, we model the source with a circular Sersic profile because we know that this is its true input profile.…”

Section: Influence Of Complexity In the Modelled Sourcementioning

confidence: 99%

Tdcosmo

Vyvere¹,

Gomer²,

Sluse³

et al. 2022

A&A

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In the context of gravitational lensing, the density profile of lensing galaxies is often considered to be perfectly elliptical. Potential angular structures are generally ignored, except to explain flux ratios of point-like sources (i.e. flux ratio anomalies). Surprisingly, the impact of azimuthal structures on extended images of the source has not been characterised, nor has its impact on the H0 inference. We address this task by creating mock images of a point source embedded in an extended source and lensed by an elliptical galaxy on which multipolar components are added to emulate boxy or discy isodensity contours. Modelling such images with a density profile free of angular structure allows us to explore the detectability of image deformation induced by the multipoles in the residual frame. Multipole deformations are almost always detectable for our highest signal-to-noise ratio (S/N) mock data. However, the detectability depends on the lens ellipticity and Einstein radius, on the S/N of the data, and on the specific lens modelling strategy. Multipoles also introduce small changes to the time-delays. We therefore quantify how undetected multipoles would impact H0 inference. When no multipoles are detected in the residuals, the impact on H0 for a given lens is in general less than a few km s−1 Mpc−1, but in the worst-case scenario, combining low S/N in the ring and large intrinsic boxyness or discyness, the bias on H0 can reach 10−12 km s−1 Mpc−1. If we now look at the inference on H0 from a population of lensing galaxies with a distribution of multipoles representative of what is found in the light profile of elliptical galaxies, we find a systematic bias on H0 of less than 1%. A comparison of our mock systems to the state-of-the-art time-delay lens sample studied by the H0LiCOW and TDCOSMO collaborations indicates that multipoles are currently unlikely to be a source of substantial systematic bias on the inferred value of H0 from time-delay lenses.

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Section: Influence Of Complexity In the Modelled Sourcementioning

confidence: 99%

Tdcosmo

Vyvere¹,

Gomer²,

Sluse³

et al. 2022

A&A

Self Cite

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“…We develop an automated data analysis pipeline that models the distribution of foreground light and mass as a sum of smooth analytic functions, and the background light as either another sum of analytic functions (e.g. Tessore et al 2016), or as a pixellated image (Warren & Dye 2003;Suyu et al 2006;Dye & Warren 2005;Vegetti & Koopmans 2009;Joseph et al 2019;Galan et al 2021). By fitting a mock sample of ∼ 500 lenses we further show that previous versions of PyAutoLens (like many lens fitting algorithms) underestimated the statistical uncertainty of lens model parameters.…”

Section: Introductionmentioning

confidence: 95%

Automated galaxy-galaxy strong lens modelling: no lens left behind

Etherington¹,

Nightingale²,

Massey³

et al. 2022

Preprint

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The distribution of dark and luminous matter can be mapped around galaxies that gravitationally lens background objects into arcs or Einstein rings. New surveys will soon observe hundreds of thousands of galaxy lenses, and current, labour-intensive analysis methods will not scale up to this challenge. We instead develop a fully automatic, Bayesian method which we use to fit a sample of 59 lenses imaged by the Hubble Space Telescope in uniform conditions. We set out to leave no lens behind and focus on ways in which automated fits fail in a small handful of lenses, describing adjustments to the pipeline that allows us to infer accurate lens models. Our pipeline ultimately fits all 59 lenses in our sample, with a high success rate key because catastrophic outliers would bias large samples with small statistical errors. Machine Learning techniques might further improve the two most difficult steps: subtracting foreground lens light and initialising a first, approximate lens model. After that, increasing model complexity is straightforward. We find a mean ∼ 1% measurement precision on the measurement of the Einstein radius across the lens sample which does not degrade with redshift up to at least z = 0.7 -in stark contrast to other techniques used to study galaxy evolution, like stellar dynamics. Our PyAutoLens software is open source, and is also installed in the Science Data Centres of the ESA Euclid mission.

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“…Particularly, three separate teams participated in the blind time-delay lens modeling challenge ) using lenstronomy. lenstronomy has seen a substantial development and incorporation of innovations and numerical recipes (Tessore & Metcalf, 2015;Shajib, 2019;Joseph et al, 2019;Galan et al, 2021;, and has found applications beyond its original aim due to the robust and high-standard design requirements.…”

Section: Statement Of Needmentioning

confidence: 99%

“…These open-source affiliated packages effectively create an ecosystem enhancing the capability of lenstronomy. They provide specified and tested solution for specific scientific investigations, such as plug-ins and direct implementation for innovative source reconstruction algorithms (SLITronomy; Joseph et al, 2019;Galan et al, 2021), gravitational wave lensing computations (lensingGW; Pagano et al, 2020), automated pipelines for gravitational lensing reconstruction (dolphin; Shajib et al, 2021a), cluster source reconstruction and local perturbative lens modeling (lenstruction; Yang et al, 2020), enhancement in large-scale structure imaging survey simulations (DESC SLSprinkler; Dark Energy Science Collaboration (LSST DESC) et al, 2021), rendering of sub-halos and line-of-sight halos (pyHalo; Gilman et al, 2020), galaxy morphology analysis (galight; Ding et al, 2020), and hierarchical analyses to measure the Hubble constant (hierArc; . With the rise in popularity and the promises in dealing with ever complex data problems with fast deep-learning methods, dedicated tools for simulating large datasets for applying such methods to strong gravitational lensing (deeplenstronomy; Morgan et al, 2021), (baobab;Park et al, 2021), as well as end-to-end Bayesian Neural Network training and validation packages for Hubble constant measurements (h0rton; Park et al, 2021), and for a hierarchical analysis of galaxy-galaxy lenses (ovejero; Wagner-Carena et al, 2021) have been developed.…”

Section: Ecosystem Of Affiliated Packagesmentioning

confidence: 99%