The supernova cosmology cookbook: Bayesian numerical recipes

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.Comment: 119 pages, 29 figures, Doctoral Thesis in Theoretical Physics, ISBN 978-91-7447-953-

Testing the mutual consistency of different supernovae surveys

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.Comment: 8 pages, 9 figures, submitted to MNRA

Comparison of cosmological parameter inference methods applied to supernovae lightcurves fitted with SALT2

We present a comparison of two methods for cosmological parameter inference from supernovae Ia lightcurves fitted with the SALT2 technique. The standard chi-square methodology and the recently proposed Bayesian hierarchical method (BHM) are each applied to identical sets of simulations based on the 3-year data release from the Supernova Legacy Survey (SNLS3), and also data from the Sloan Digital Sky Survey (SDSS), the Low Redshift sample and the Hubble Space Telescope (HST), assuming a concordance LCDM 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 lightcurves, suffer from small biasses. The magnitude of the biasses is similar in both cases, with the BHM yielding slightly more accurate results, in particular for cosmological parameters when applied to just the SNLS3 single survey data sets. Most notably, in this case, the biasses in the recovered matter density $\Omega_{\rm m,0}$ are in opposite directions for the two methods. For any given realisation of the SNLS3-type data, this can result in a $\sim 2 \sigma$ discrepancy in the estimated value of $\Omega_{\rm m,0}$ 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.Comment: 10 pages, 7 figures, submitted to MNRA

Bayesian constraints on dark matter halo properties using gravitationally-lensed supernovae

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 {\sc 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 halo parameters in our simulations, we find that a catalogue analogous to the existing SNLS3 data set is incapable of detecting the lensing signal, but a catalogue containing approximately three times as many SNIa does produce robust and accurate parameter constraints and model selection results for two halo models: a truncated singular isothermal sphere (SIS) and a Navarro--Frenk--White (NFW) profile, thereby validating our analysis methodology. 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 obtained from the equivalent simulated SNIa data set. This difference is driven by a preferred value of $\eta \approx 1$ in the assumed scaling-law $\sigma \propto L^\eta$ between velocity dispersion and luminosity, which is somewhat higher than the canonical values of $\eta = \tfrac{1}{4}$ and $\eta = \tfrac{1}{3}$ for early and late-type galaxies, respectively, and leads to a stronger lensing effect by the halo. No detection of a lensing signal is made for the NFW model.Comment: Revised version matches manuscript accepted by MNRAS: Manuscript ID: MN-12-1716-MJ.R

DES14X3taz: a type I superluminous supernova showing a luminous, rapidly cooling initial pre-peak bump

We present DES14X3taz, a new hydrogen-poor superluminous supernova (SLSN-I) discovered by the Dark Energy Survey (DES) supernova program, with additional photometric data provided by the Survey Using DECam for Superluminous Supernovae. Spectra obtained using Optical System for Imaging and low-Intermediate-Resolution Integrated Spectroscopy on the Gran Telescopio CANARIAS show DES14X3taz is an SLSN-I at z = 0.608. Multi-color photometry reveals a double-peaked light curve: a blue and relatively bright initial peak that fades rapidly prior to the slower rise of the main light curve. Our multi-color photometry allows us, for the first time, to show that the initial peak cools from 22,000 to 8000 K over 15 rest-frame days, and is faster and brighter than any published core-collapse supernova, reaching 30% of the bolometric luminosity of the main peak. No physical 56Ni-powered model can fit this initial peak. We show that a shock-cooling model followed by a magnetar driving the second phase of the light curve can adequately explain the entire light curve of DES14X3taz. Models involving the shock-cooling of extended circumstellar material at a distance of ~=400 {\text{}}{R}&sun; are preferred over the cooling of shock-heated surface layers of a stellar envelope. We compare DES14X3taz to the few double-peaked SLSN-I events in the literature. Although the rise times and characteristics of these initial peaks differ, there exists the tantalizing possibility that they can be explained by one physical interpretation

Bahamas: new analysis of type la supernovae reveals inconsistencies with standard cosmology

We present results obtained by applying our Bayesian HierArchical Modeling for the Analysis of Supernova cosmology (Bahamas) software package to the 740 spectroscopically confirmed supernovae of type Ia (SNe Ia) from the "Joint Light-curve Analysis" (JLA) data set. We simultaneously determine cosmological parameters and standardization parameters, including corrections for host galaxy mass, residual scatter, and object-by-object intrinsic magnitudes. Combining JLA and Planck data on the cosmic microwave background, we find significant discrepancies in cosmological parameter constraints with respect to the standard analysis: we find \u3a9m = 0.399 \ub1 0.027, 2.8\u3c3, higher than previously reported, and w = -0.910 \ub1 0.045, 1.6\u3c3, higher than the standard analysis. We determine the residual scatter to be \u3c3res = 0.104 \ub1 0.005. We confirm (at the 95% probability level) the existence of two subpopulations segregated by host galaxy mass, separated at log10 (M/M 99) = 10, differing in mean intrinsic magnitude by 0.055 \ub1 0.022 mag, lower than previously reported. Cosmological parameter constraints, however, are unaffected by the inclusion of corrections for host galaxy mass. We find 3c4\u3c3 evidence for a sharp drop in the value of the color correction parameter, \u3b2 (z), at a redshift zt = 0.662 \ub1 0.055. We rule out some possible explanations for this behavior, which remains unexplained