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The supernova cosmology cookbook: Bayesian numerical recipes
Stockholm University, Faculty of Science, Department of Physics.
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: urn:nbn:se:su:diva-106710ISBN: 978-91-7447-953-9 (print)OAI: oai:DiVA.org:su-106710DiVA: diva2:738282
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At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 4: Submitted.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
List of papers
1. Comparison of cosmological parameter inference methods applied to supernovae light curves fitted with salt-II
Open this publication in new window or tab >>Comparison of cosmological parameter inference methods applied to supernovae light curves fitted with salt-II
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.

Keyword
methods: data analysis, methods: statistical, supernovae: general, cosmology: miscellaneous
National Category
Astronomy, Astrophysics and Cosmology
Research subject
Physics
Identifiers
urn:nbn:se:su:diva-100372 (URN)10.1093/mnras/stt2114 (DOI)000329177100025 ()
Note

AuthorCount:4;

Available from: 2014-02-04 Created: 2014-02-03 Last updated: 2017-12-06Bibliographically approved
2. Bayesian constraints on dark matter halo properties using gravitationally lensed supernovae
Open this publication in new window or tab >>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
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.

Keyword
gravitational lensing: weak, methods: data analysis, methods: statistical, supernovae: general, galaxies: haloes
National Category
Astronomy, Astrophysics and Cosmology
Research subject
Physics
Identifiers
urn:nbn:se:su:diva-93309 (URN)10.1093/mnras/sts700 (DOI)000322405900001 ()
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
3. A simple and robust method for automated photometric classification of supernovae using neural networks
Open this publication in new window or tab >>A simple and robust method for automated photometric classification of supernovae using neural networks
2013 (English)In: Monthly notices of the Royal Astronomical Society, ISSN 0035-8711, E-ISSN 1365-2966, Vol. 429, no 2, 1278-1285 p.Article in journal (Refereed) Published
Abstract [en]

A method is presented for automated photometric classification of supernovae (SNe) as Type Ia or non-Ia. A two-step approach is adopted in which (i) the SN light curve flux measurements in each observing filter are fitted separately to an analytical parametrized function that is sufficiently flexible to accommodate virtually all types of SNe and (ii) the fitted function parameters and their associated uncertainties, along with the number of flux measurements, the maximum-likelihood value of the fit and Bayesian evidence for the model, are used as the input feature vector to a classification neural network that outputs the probability that the SN under consideration is of Type Ia. The method is trained and tested using data released following the Supernova Photometric Classification Challenge (SNPCC), consisting of light curves for 20 895 SNe in total. We consider several random divisions of the data into training and testing sets: for instance, for our sample D-1 (D-4), a total of 10 (40) per cent of the data are involved in training the algorithm and the remainder used for blind testing of the resulting classifier; we make no selection cuts. Assigning a canonical threshold probability of p(th) = 0.5 on the network output to class an SN as Type Ia, for the sample D-1 (D-4) we obtain a completeness of 0.78 (0.82), purity of 0.77 (0.82) and SNPCC figure of merit of 0.41 (0.50). Including the SN host-galaxy redshift and its uncertainty as additional inputs to the classification network results in a modest 5-10 per cent increase in these values. We find that the quality of the classification does not vary significantly with SN redshift. Moreover, our probabilistic classification method allows one to calculate the expected completeness, purity and figure of merit (or other measures of classification quality) as a function of the threshold probability p(th), without knowing the true classes of the SNe in the testing sample, as is the case in the classification of real SNe data. The method may thus be improved further by optimizing p(th) and can easily be extended to divide non-Ia SNe into their different classes.

Keyword
methods: data analysis, methods: statistical, supernovae: general
National Category
Astronomy, Astrophysics and Cosmology
Research subject
Physics
Identifiers
urn:nbn:se:su:diva-90805 (URN)10.1093/mnras/sts412 (DOI)000318239300029 ()
Funder
Swedish Research Council, 621-2010-3301
Note

AuthorCount:3;

Available from: 2013-06-12 Created: 2013-06-11 Last updated: 2017-12-06Bibliographically approved
4. Testing the consistency of different supernovae surveys
Open this publication in new window or tab >>Testing the consistency of different supernovae surveys
(English)In: Monthly notices of the Royal Astronomical Society, ISSN 0035-8711, E-ISSN 1365-2966Article in journal (Refereed) Submitted
Abstract [en]

It is now common practice to constrain cosmological parameters using supernovae (SNe) catalogues constructed from several different surveys. Before performing such a joint analysis, however, one should check that parameter constraints derived from the individual SNe surveys that make up the catalogue are mutually consistent. We describe a statistically-robust mutual consistency test, which we calibrate using simulations, and apply it to each pairwise combination of the surveys making up, respectively, the UNION2 catalogue and the very recent JLA compilation by Betoule et al. We find no inconsistencies in the latter case, but conclusive evidence for inconsistency between some survey pairs in the UNION2 catalogue.

National Category
Astronomy, Astrophysics and Cosmology
Research subject
Physics
Identifiers
urn:nbn:se:su:diva-106708 (URN)
Available from: 2014-08-17 Created: 2014-08-17 Last updated: 2017-12-05
5. Photometric classification of supernovae using a hierarchical neural network method
Open this publication in new window or tab >>Photometric classification of supernovae using a hierarchical neural network method
(English)Manuscript (preprint) (Other academic)
National Category
Astronomy, Astrophysics and Cosmology
Research subject
Physics
Identifiers
urn:nbn:se:su:diva-106709 (URN)
Available from: 2014-08-17 Created: 2014-08-17 Last updated: 2014-08-18

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