分类： 天文学 >> 天文学 提交时间： 2023-02-19
Amy Etherington
James W. Nightingale
Richard Massey
Sut-Ieng Tam
XiaoYue Cao
Anna Niemiec
Qiuhan He
Andrew Robertson
Ran Li
Aristeidis Amvrosiadis
Shaun Cole
Jose M. Diego
Carlos S. Frenk
Brenda L. Frye
David Harvey
Mathilde Jauzac
Anton M. Koekemoer
David J. Lagattuta
Marceau Limousin
Guillaume Mahler
摘要： The distribution of mass in galaxy-scale strong gravitational lenses is often modelled as an elliptical power law plus `external shear', which notionally accounts for neighbouring galaxies and cosmic shear. We show that it does not. Except in a handful of rare systems, the best-fit values of external shear do not correlate with independent measurements of shear: from weak lensing in 45 Hubble Space Telescope images, or in 50 mock images of lenses with complex distributions of mass. Instead, the best-fit shear is aligned with the major or minor axis of 88% of lens galaxies; and the amplitude of the external shear increases if that galaxy is disky. We conclude that `external shear' attached to a power law model is not physically meaningful, but a fudge to compensate for lack of model complexity. Since it biases other model parameters that are interpreted as physically meaningful in several science analyses (e.g. measuring galaxy evolution, dark matter physics or cosmological parameters), we recommend that future studies of galaxy-scale strong lensing should employ more flexible mass models.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Amy Etherington
James W. Nightingale
Richard Massey
XiaoYue Cao
Andrew Robertson
Nicola C. Amorisco
Aristeidis Amvrosiadis
Shaun Cole
Carlos S. Frenk
Qiuhan He
Ran Li
Sut-Ieng Tam
摘要： 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 \textit{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 {\em 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 $\sim1\%$ measurement precision on the measurement of the Einstein radius across the lens sample which {\em 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 \texttt{PyAutoLens} software is open source, and is also installed in the Science Data Centres of the ESA Euclid mission.
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