No Arabic abstract
Much of the cosmological utility thus far extracted from Type Ia supernovae (SNe Ia) relies on the assumption that SN~Ia peak luminosities do not evolve significantly with the age (local or global) of their stellar environments. Two recent studies have provided conflicting results in evaluating the validity of this assumption, with one finding no correlation between Hubble residuals (HR) and stellar environment age, while the other claims a significant correlation. In this Letter we perform an independent reanalysis that rectifies issues with the statistical methods employed by both of the aforementioned studies. Our analysis follows a principled approach that properly accounts for regression dilution and critically (and unlike both prior studies) utilises the Bayesian-model-produced SN environment age estimates (posterior samples) instead of point estimates. Moreover, the posterior is used as an informative prior in the regression. We find the Pearson correlation between the HR and local (global) age to be in excess of $4sigma$ ($3sigma$). Assuming there exists a linear relationship between HR and local (global) age, we find a corresponding slope of $-0.035 pm 0.007,mathrm{mag,Gyr}^{-1}$ ($-0.036 pm 0.007,mathrm{mag,Gyr}^{-1}$). We encourage further usage of our approach to examine possible cosmological implications of the HR and age correlation.
We present a comprehensive statistical analysis of the properties of Type Ia SN light curves in the near infrared using recent data from PAIRITEL and the literature. We construct a hierarchical Bayesian framework, incorporating several uncertainties including photometric error, peculiar velocities, dust extinction and intrinsic variations, for coherent statistical inference. SN Ia light curve inferences are drawn from the global posterior probability of parameters describing both individual supernovae and the population conditioned on the entire SN Ia NIR dataset. The logical structure of the hierarchical model is represented by a directed acyclic graph. Fully Bayesian analysis of the model and data is enabled by an efficient MCMC algorithm exploiting the conditional structure using Gibbs sampling. We apply this framework to the JHK_s SN Ia light curve data. A new light curve model captures the observed J-band light curve shape variations. The intrinsic variances in peak absolute magnitudes are: sigma(M_J) = 0.17 +/- 0.03, sigma(M_H) = 0.11 +/- 0.03, and sigma(M_Ks) = 0.19 +/- 0.04. We describe the first quantitative evidence for correlations between the NIR absolute magnitudes and J-band light curve shapes, and demonstrate their utility for distance estimation. The average residual in the Hubble diagram for the training set SN at cz > 2000 km/s is 0.10 mag. The new application of bootstrap cross-validation to SN Ia light curve inference tests the sensitivity of the model fit to the finite sample and estimates the prediction error at 0.15 mag. These results demonstrate that SN Ia NIR light curves are as effective as optical light curves, and, because they are less vulnerable to dust absorption, they have great potential as precise and accurate cosmological distance indicators.
A string of recent studies has debated the exact form and physical origin of an evolutionary trend between the peak luminosity of Type Ia supernovae (SNe Ia) and the properties of the galaxies that host them. We shed new light on the discussion by presenting an analysis of ~200 low-redshift SNe Ia in which we measure the separation of Hubble residuals (HR; as probes of luminosity) between two host-galaxy morphological types. We show that this separation can test the predictions made by recently proposed models, using an independently and empirically determined distribution of each morphological type in host-property space. Our results are partially consistent with the new HR--age slope, but we find significant scatter in the predictions from different galaxy catalogues. The inconsistency in age illuminates an issue in the current debate that was not obvious in the long-discussed mass models: HR--host-property models are strongly dependent on the methods employed to determine galaxy properties. While our results demonstrate the difficulty in constructing a universal model for age as a proxy for host environment, our results indeed identify evolutionary trends between mass, age, morphology, and HR values, encouraging (or requiring, if such trends are to be accounted for in cosmological studies) further investigation.
The detailed nature of type Ia supernovae (SNe Ia) remains uncertain, and as survey statistics increase, the question of astrophysical systematic uncertainties arises, notably that of the evolution of SN Ia populations. We study the dependence on redshift of the SN Ia light-curve stretch, a purely intrinsic SN property, to probe its potential redshift drift. The SN stretch has been shown to be strongly correlated with the SN environment, notably with stellar age tracers. We modeled the underlying stretch distribution as a function of redshift, using the evolution of the fraction of young and old SNe Ia as predicted using the SNfactory dataset, and assuming a constant underlying stretch distribution for each age population consisting of Gaussian mixtures. We tested our prediction against published samples that were cut to have marginal magnitude selection effects so that any observed change is indeed astrophysical and not observational in origin. In this first study, there are indications that the underlying SN Ia stretch distribution evolves as a function of redshift, and that the age drifting model is a better description of the data than any time-constant model, including the sample-based asymmetric distributions that are often used to correct Malmquist bias at a significance higher than 5 $sigma$. The favored underlying stretch model is a bimodal one, composed of a high-stretch mode shared by both young and old environments, and a low-stretch mode that is exclusive to old environments. The precise effect of the redshift evolution of the intrinsic properties of a SN Ia population on cosmology remains to be studied. The astrophysical drift of the SN stretch distribution does affect current Malmquist bias corrections and hence the distances that are derived using SNe that are affected by observational selection effects. This bias increases with surveys covering larger redshift ranges.
We present a measurement of the volumetric Type Ia supernova (SN Ia) rate (SNR_Ia) as a function of redshift for the first four years of data from the Canada-France-Hawaii Telescope (CFHT) Supernova Legacy Survey (SNLS). This analysis includes 286 spectroscopically confirmed and more than 400 additional photometrically identified SNe Ia within the redshift range 0.1<z<1.1. The volumetric SNR_Ia evolution is consistent with a rise to z~1.0 that follows a power-law of the form (1+z)^alpha, with alpha=2.11+/-0.28. This evolutionary trend in the SNLS rates is slightly shallower than that of the cosmic star-formation history over the same redshift range. We combine the SNLS rate measurements with those from other surveys that complement the SNLS redshift range, and fit various simple SN Ia delay-time distribution (DTD) models to the combined data. A simple power-law model for the DTD (i.e., proportional to t^-beta) yields values from beta=0.98+/-0.05 to beta=1.15+/-0.08 depending on the parameterization of the cosmic star formation history. A two-component model, where SNR_Ia is dependent on stellar mass (Mstellar) and star formation rate (SFR) as SNR_Ia(z)=AxMstellar(z) + BxSFR(z), yields the coefficients A=1.9+/-0.1 SNe/yr/M_solar and B=3.3+/-0.2 SNe/yr/(M_solar/yr). More general two-component models also fit the data well, but single Gaussian or exponential DTDs provide significantly poorer matches. Finally, we split the SNLS sample into two populations by the light curve width (stretch), and show that the general behavior in the rates of faster-declining SNe Ia (0.8<s<1.0) is similar, within our measurement errors, to that of the slower objects (1.0<s<1.3) out to z~0.8.
The standard cosmology strongly relies upon the Cosmological Principle, which consists on the hypotheses of large scale isotropy and homogeneity of the Universe. Testing these assumptions is, therefore, crucial to determining if there are deviations from the standard cosmological paradigm. In this paper, we use the latest type Ia supernova compilations, namely JLA and Union2.1 to test the cosmological isotropy at low redshift ranges ($z<0.1$). This is performed through a Bayesian selection analysis, in which we compare the standard, isotropic model, with another one including a dipole correction due to peculiar velocities. We find that the Union2.1 sample favors the dipole-corrected model, but the opposite happens for the JLA. Nonetheless, the velocity dipole results are in good agreement with previous analyses carried out with both datasets. We conclude that there are no significant indications for large anisotropic signals from nearby supernova compilations, albeit this test should be greatly improved with the upcoming cosmological surveys.