Bayesian constraints on dark matter halo properties using gravitationally lensed supernovae
2013 (English)In: Monthly notices of the Royal Astronomical Society, ISSN 0035-8711, E-ISSN 1365-2966, Vol. 433, no 4, 2693-2705 p.Article in journal (Refereed) Published
A hierarchical Bayesian method is applied to the analysis of Type Ia supernovae (SNIa) observations to constrain the properties of the dark matter haloes of galaxies along the SNIa lines of sight via their gravitational lensing effect. The full joint posterior distribution of the dark matter halo parameters is explored using the nested sampling algorithm MultiNest, which also efficiently calculates the Bayesian evidence, thereby facilitating robust model comparison. We first demonstrate the capabilities of the method by applying it to realistic simulated SNIa data, based on the real 3-year data release from the Supernova Legacy Survey (SNLS3). Assuming typical values for the parameters in a truncated singular isothermal sphere (SIS) halo model, we find that a catalogue analogous to the existing SNLS3 data set is typically incapable of detecting the lensing signal, but a catalogue containing approximately three times as many SNIa can produce robust and accurate parameter constraints and lead to a clear preference for the SIS halo model over a model that assumes no lensing. In the analysis of the real SNLS3 data, contrary to previous studies, we obtain only a very marginal detection of a lensing signal and weak constraints on the halo parameters for the truncated SIS model, although these constraints are tighter than those typically obtained from equivalent simulated SNIa data sets. This difference is driven by a preferred value of eta approximate to 1 in the assumed scaling law sigma proportional to L-eta between velocity dispersion and luminosity, which is somewhat higher than the canonical values of eta = 1/4 and eta = 1/3 for early and late-type galaxies, respectively.
Place, publisher, year, edition, pages
2013. Vol. 433, no 4, 2693-2705 p.
gravitational lensing: weak, methods: data analysis, methods: statistical, supernovae: general, galaxies: haloes
Astronomy, Astrophysics and Cosmology
Research subject Physics
IdentifiersURN: urn:nbn:se:su:diva-93309DOI: 10.1093/mnras/sts700ISI: 000322405900001OAI: oai:DiVA.org:su-93309DiVA: diva2:646100
FunderSwedish Research Council, 621-2010-3301