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Bayesian constraints on dark matter halo properties using gravitationally lensed supernovae
Stockholm University, Faculty of Science, Department of Physics. Stockholm University, Faculty of Science, The Oskar Klein Centre for Cosmo Particle Physics (OKC).
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
Abstract [en]

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.
Keyword [en]
gravitational lensing: weak, methods: data analysis, methods: statistical, supernovae: general, galaxies: haloes
National Category
Astronomy, Astrophysics and Cosmology
Research subject
Physics
Identifiers
URN: urn:nbn:se:su:diva-93309DOI: 10.1093/mnras/sts700ISI: 000322405900001OAI: oai:DiVA.org:su-93309DiVA: diva2:646100
Funder
Swedish Research Council, 621-2010-3301
Note

AuthorCount:4;

Available from: 2013-09-06 Created: 2013-09-06 Last updated: 2017-12-06Bibliographically approved
In thesis
1. The supernova cosmology cookbook: Bayesian numerical recipes
Open this publication in new window or tab >>The supernova cosmology cookbook: Bayesian numerical recipes
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Theoretical and observational cosmology have enjoyed a number of significant successes over the last two decades. Cosmic microwave background measurements from the Wilkinson Microwave Anisotropy Probe and Planck, together with large-scale structure and supernova (SN) searches, have put very tight constraints on cosmological parameters. Type Ia supernovae (SNIa) played a central role in the discovery of the accelerated expansion of the Universe, recognised by the Nobel Prize in Physics in 2011.The last decade has seen an enormous increase in the amount of high quality SN observations, with SN catalogues now containing hundreds of objects. This number is expected to increase to thousands in the next few years, as data from next-generation missions, such as the Dark Energy Survey and Large Synoptic Survey Telescope become available. In order to exploit the vast amount of forthcoming high quality data, it is extremely important to develop robust and efficient statistical analysis methods to answer cosmological questions, most notably determining the nature of dark energy.To address these problems my work is based on nested-sampling approaches to parameter estimation and model selection and neural networks for machine-learning. Using advanced Bayesian techniques, I constrain the properties of dark-matter haloes along the SN lines-of-sight via their weak gravitational lensing effects, develop methods for classifying SNe photometrically from their lightcurves, and present results on more general issues associated with constraining cosmological parameters and testing the consistency of different SN compilations.

Place, publisher, year, edition, pages
Stockholm: Department of Physics, Stockholm University, 2014. 98 p.
National Category
Astronomy, Astrophysics and Cosmology
Research subject
Theoretical Physics
Identifiers
urn:nbn:se:su:diva-106710 (URN)978-91-7447-953-9 (ISBN)
Public defence
2014-09-26, FB41, AlbaNova universitetscentrum, Roslagstullsbacken 21, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 4: Submitted. Paper 5: Manuscript.

Available from: 2014-09-04 Created: 2014-08-17 Last updated: 2014-08-29Bibliographically approved

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Karpenka, Natallia V.
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