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Comparison of cosmological parameter inference methods applied to supernovae light curves fitted with salt-II
Stockholm University, Faculty of Science, Department of Physics. Stockholm University, Faculty of Science, The Oskar Klein Centre for Cosmo Particle Physics (OKC).
2014 (English)In: Monthly notices of the Royal Astronomical Society, ISSN 0035-8711, E-ISSN 1365-2966, Vol. 437, no 4, 3298-3311 p.Article in journal (Refereed) Published
Abstract [en]

We present a comparison of two methods for cosmological parameter inference from Type Ia supernovae (SNeIa) light curves fitted with the salt-ii technique, in which we treat the statistical errors but not the systematic errors. The standard chi(2) methodology and the recently proposed SNeIa Bayesian hierarchical method (SNBHM) are each applied to identical sets of simulations based on the 3-yr data release from the Supernova Legacy Survey (SNLS3), and also data from the Sloan Digital Sky Survey, the low-redshift sample and the Hubble Space Telescope, assuming a concordance Lambda cold dark matter cosmology. For both methods, we find that the recovered values of the cosmological parameters, and the global nuisance parameters controlling the stretch and colour corrections to the supernovae light curves, suffer from small biases. The magnitude of the biases is similar in both cases, with the SNBHM yielding slightly more accurate results for cosmological parameters when applied to just the SNLS3 single survey data sets. Most notably, in this case, the biases in the recovered matter density (m,0) are in opposite directions for the two methods. For any given realization of the SNLS3-type data, this can result in a similar to 2 Sigma discrepancy in the estimated value of (m,0) between the two methods, which we find to be the case for real SNLS3 data. As more higher and lower redshift SNIa samples are included, however, the cosmological parameter estimates of the two methods converge.

Place, publisher, year, edition, pages
2014. Vol. 437, no 4, 3298-3311 p.
Keyword [en]
methods: data analysis, methods: statistical, supernovae: general, cosmology: miscellaneous
National Category
Astronomy, Astrophysics and Cosmology
Research subject
Physics
Identifiers
URN: urn:nbn:se:su:diva-100372DOI: 10.1093/mnras/stt2114ISI: 000329177100025OAI: oai:DiVA.org:su-100372DiVA: diva2:693487
Note

AuthorCount:4;

Available from: 2014-02-04 Created: 2014-02-03 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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