AffiliationUniv Arizona, Dept Phys, Appl Math Program
Univ Arizona, Dept Astron
gravitational lensing: strong
MetadataShow full item record
PublisherIOP PUBLISHING LTD
CitationRuan, C. Z., Melia, F., & Zhang, T. J. (2018). Model-independent Test of the Cosmic Distance Duality Relation. The Astrophysical Journal, 866(1), 31.
JournalThe Astrophysical Journal
Rights© 2018. The American Astronomical Society. All rights reserved.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractA validation of the cosmic distance duality (CDD) relation, h() ( ) () () z zdz dz º+ = 1 A L 1 2 , coupling the luminosity (dL) and angular-diameter (dA) distances, is crucial because its violation would require exotic new physics. We present a model-independent test of the CDD, based on strong lensing and a reconstruction of the H II galaxy Hubble diagram using Gaussian processes, to confirm the validity of the CDD at a very high level of confidence. Using parameterizations h( )z z = +1 h0 and h( )z zz =+ + 1 h h 1 2 2, our best-fit results are h = - + 0.0147 0 0.066 0.056, and h = - + 0.1091 1 0.1568 0.1680 and h = - - + 0.0603 2 0.0988 0.0999, respectively. In spite of these strong constraints, however, we also point out that the analysis of strong lensing using a simplified single isothermal sphere (SIS) model for the lens produces some irreducible scatter in the inferred CDD data. The use of an extended SIS approximation, with a power-law density structure, yields very similar results, but does not lessen the scatter due to its larger number of free parameters, which weakens the best-fit constraints. Future work with these strong lenses should therefore be based on more detailed ray-tracing calculations to determine the mass distribution more precisely
VersionFinal accepted manuscript
SponsorsNational Key R & D Program of China (2017YFA0402600), the National Science Foundation of China (Grants No. 11573006, 11528306), the Fundamental Research Funds for the Central Universities and the Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase).