On modeling galaxy-scale strong lens systems

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

I review methods for modeling gravitational lens systems comprising multiple images of a background source surrounding a foreground galaxy. In a Bayesian framework, the likelihood is driven by the nature of the data, which in turn depends on whether the source is point-like or extended. The prior encodes astrophysical expectations about lens galaxy mass distributions, either through a careful choice of model families, or through an explicit Bayesian prior applied to under-constrained free-form models. We can think about different lens modeling methods in terms of their choices of likelihoods and priors.

Original languageEnglish (US)
Pages (from-to)2151-2176
Number of pages26
JournalGeneral Relativity and Gravitation
Volume42
Issue number9
DOIs
StatePublished - Jun 30 2010

Fingerprint

lenses
galaxies
gravitational lenses
mass distribution
point sources
astrophysics

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy (miscellaneous)

Cite this

@article{e32f35b1070548489d19546bc4452028,
title = "On modeling galaxy-scale strong lens systems",
abstract = "I review methods for modeling gravitational lens systems comprising multiple images of a background source surrounding a foreground galaxy. In a Bayesian framework, the likelihood is driven by the nature of the data, which in turn depends on whether the source is point-like or extended. The prior encodes astrophysical expectations about lens galaxy mass distributions, either through a careful choice of model families, or through an explicit Bayesian prior applied to under-constrained free-form models. We can think about different lens modeling methods in terms of their choices of likelihoods and priors.",
author = "Charles Keeton",
year = "2010",
month = "6",
day = "30",
doi = "https://doi.org/10.1007/s10714-010-1041-1",
language = "English (US)",
volume = "42",
pages = "2151--2176",
journal = "General Relativity and Gravitation",
issn = "0001-7701",
publisher = "Springer New York",
number = "9",

}

On modeling galaxy-scale strong lens systems. / Keeton, Charles.

In: General Relativity and Gravitation, Vol. 42, No. 9, 30.06.2010, p. 2151-2176.

Research output: Contribution to journalArticle

TY - JOUR

T1 - On modeling galaxy-scale strong lens systems

AU - Keeton, Charles

PY - 2010/6/30

Y1 - 2010/6/30

N2 - I review methods for modeling gravitational lens systems comprising multiple images of a background source surrounding a foreground galaxy. In a Bayesian framework, the likelihood is driven by the nature of the data, which in turn depends on whether the source is point-like or extended. The prior encodes astrophysical expectations about lens galaxy mass distributions, either through a careful choice of model families, or through an explicit Bayesian prior applied to under-constrained free-form models. We can think about different lens modeling methods in terms of their choices of likelihoods and priors.

AB - I review methods for modeling gravitational lens systems comprising multiple images of a background source surrounding a foreground galaxy. In a Bayesian framework, the likelihood is driven by the nature of the data, which in turn depends on whether the source is point-like or extended. The prior encodes astrophysical expectations about lens galaxy mass distributions, either through a careful choice of model families, or through an explicit Bayesian prior applied to under-constrained free-form models. We can think about different lens modeling methods in terms of their choices of likelihoods and priors.

UR - http://www.scopus.com/inward/record.url?scp=77955844497&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77955844497&partnerID=8YFLogxK

U2 - https://doi.org/10.1007/s10714-010-1041-1

DO - https://doi.org/10.1007/s10714-010-1041-1

M3 - Article

VL - 42

SP - 2151

EP - 2176

JO - General Relativity and Gravitation

JF - General Relativity and Gravitation

SN - 0001-7701

IS - 9

ER -