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Photometric classification of supernovae using a hierarchical neural network method
Stockholm University, Faculty of Science, Department of Physics. Stockholm University, Faculty of Science, The Oskar Klein Centre for Cosmo Particle Physics (OKC).
(English)Manuscript (preprint) (Other academic)
National Category
Astronomy, Astrophysics and Cosmology
Research subject
Physics
Identifiers
URN: urn:nbn:se:su:diva-106709OAI: oai:DiVA.org:su-106709DiVA: diva2:738281
Available from: 2014-08-17 Created: 2014-08-17 Last updated: 2014-08-18
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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Citation style
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