2019
DOI: 10.1093/mnras/stz2220
|View full text |Cite
|
Sign up to set email alerts
|

Galaxy structure with strong gravitational lensing: decomposing the internal mass distribution of massive elliptical galaxies

Abstract: We investigate how strong gravitational lensing can test contemporary models of massive elliptical (ME) galaxy formation, by combining a traditional decomposition of their visible stellar distribution with a lensing analysis of their mass distribution. As a proof of concept, we study a sample of three ME lenses, observing that all are composed of two distinct baryonic structures, a 'red' central bulge surrounded by an extended envelope of stellar material. Whilst these two components look photometrically simil… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

1
34
0

Year Published

2020
2020
2024
2024

Publication Types

Select...
9
1

Relationship

4
6

Authors

Journals

citations
Cited by 38 publications
(35 citation statements)
references
References 110 publications
1
34
0
Order By: Relevance
“…Furthermore, by modeling galaxy-scale strong lenses as the sum of luminous and dark components, the mass-to-light ratio and inner density profile of the dark matter halo can be simultaneously constrained (e.g., Auger et al 2010;Sonnenfeld et al 2019;Shajib et al 2020). Such measurements for a large number of lensing systems over a wide range of redshifts make it possible to study the structural evolution of massive elliptical galaxies, and possibly in the future, lenses of any Hubble type (e.g., Sonnenfeld et al 2015;Nightingale et al 2019). For nearby strong lensing galaxies, extragalactic tests of general relativity can be performed by combining lens modeling with spatially resolved stellar kinematic observations (Collett et al 2018).…”
Section: Introductionmentioning
confidence: 99%
“…Furthermore, by modeling galaxy-scale strong lenses as the sum of luminous and dark components, the mass-to-light ratio and inner density profile of the dark matter halo can be simultaneously constrained (e.g., Auger et al 2010;Sonnenfeld et al 2019;Shajib et al 2020). Such measurements for a large number of lensing systems over a wide range of redshifts make it possible to study the structural evolution of massive elliptical galaxies, and possibly in the future, lenses of any Hubble type (e.g., Sonnenfeld et al 2015;Nightingale et al 2019). For nearby strong lensing galaxies, extragalactic tests of general relativity can be performed by combining lens modeling with spatially resolved stellar kinematic observations (Collett et al 2018).…”
Section: Introductionmentioning
confidence: 99%
“…For instance, it can be used in combination with dynamical analysis to determine the total mass density profiles of the lens systems (e.g., Koopmans et al 2006Koopmans et al , 2009Auger et al 2010;Bolton et al 2012;Li et al 2018). In case an independent inference on the stellar mass of the deflectors is available, e.g., via stellar population analysis, SL also allows one to directly measure the amount and properties of the internal dark matter of the deflectors (e.g., Koopmans et al 2006;Auger et al 2009;Tortora et al 2010;Spiniello et al 2011; Barnabè et al 2012;Shu et al 2015;Gilman et al 2018;Nightingale et al 2019;Schuldt et al 2019).…”
Section: Introductionmentioning
confidence: 99%
“…For many modeling problems the model comprises abstract model components representing objects or processes in a physical system. For example, galaxy morphology studies in astrophysics where model components represent the light profile of stars (Häußler et al, 2013;Nightingale et al, 2019). For these problems the likelihood function is typically a sequence of numerical processes (e.g., convolutions, Fourier transforms, linear algebra) and extensions to the model often requires the addition of new model components in a way that is non-trivially included in the fitting process and likelihood function.…”
Section: Model Abstraction and Compositionmentioning
confidence: 99%