Joint Light-curve Analysis Research Articles

Sampling the probability distribution of Type Ia Supernova lightcurve parameters in cosmological analysis

In order to obtain robust cosmological constraints from Type Ia supernova (SN Ia) data, we have applied Markov Chain Monte Carlo (MCMC) to SN Ia lightcurve fitting. We develop a method for sampling the resultant probability density distributions (pdf) of the SN Ia lightcuve parameters in the MCMC likelihood analysis to constrain cosmological parameters, and validate it using simulated data sets. Applying this method to the Joint Lightcurve Analysis (JLA) data set of SNe Ia, we find that sampling the SN Ia lightcurve parameter pdf's leads to cosmological parameters closer to that of a flat Universe with a cosmological constant, compared to the usual practice of using only the best fit values of the SN Ia lightcurve parameters. Our method will be useful in the use of SN Ia data for precision cosmology.

DGP cosmological model with generalized Ricci dark energy

The braneworld model proposed by Dvali, Gabadadze and Porrati (DGP) leads to an accelerated universe without cosmological constant or other form of dark energy for the positive branch $(\epsilon =+1)$. For the negative branch $(\epsilon =-1)$ we have investigated the behavior of a model with an holographic Ricci-like dark energy and dark matter, where the IR cutoff takes the form $\alpha H^2 + \beta \dot{H}$, being $H$ the Hubble parameter and $\alpha$, $\beta$ positive constants of the model. We perform an analytical study of the model in the late-time dark energy dominated epoch, where we obtain a solution for $r_cH(z)$, where $r_c$ is the leakage scale of gravity into the bulk, and conditions for the negative branch on the holographic parameters $\alpha$ and $\beta$, in order to hold the conditions of weak energy and accelerated universe. On the other hand, we compare the model versus the late-time cosmological data using the latest type Ia supernova sample of the Joint Light-curve Analysis (JLA), in order to constraint the holographic parameters in the negative branch, as well as $r_cH_0$ in the positive branch, where $H_0$ is the Hubble constant. We find that the model has a good fit to the data and that the most likely values for $(r_cH_0, \alpha, \beta)$ lie in the permitted region found from an analytical solution in a dark energy dominated universe. We give a justification to use holographic cut-off in 4D for the dark energy in the 5 dimensional DGP model. Finally, using the Bayesian Information Criterion we find that this model it is disfavored compared with the flat $\Lambda$CDM model.

First measurement of σ8 using supernova magnitudes only

Abstract A method was recently proposed which allows the conversion of the weak-lensing effects in the Type Ia supernova (SNeIa) Hubble diagram from noise into signal. Such signal is sensitive to the growth of structure in the universe, and in particular can be used as a measurement of σ8 independently from more traditional methods such as those based on the cosmic microwave background, cosmic shear or cluster abundance. We extend here that analysis to allow for intrinsic non-Gaussianities in the supernova probability distribution function, and discuss how this can be best modelled using the Bayes factor. Although it was shown that a precise measurement of σ8 requires ∼105 SNeIa, current data already allow an important proof of principle. In particular, we make use of the 706 supernovae with z ≤ 0.9 of the recent Joint Lightcurve Analysis catalogue and show that a simple treatment of intrinsic non-Gaussianities with a couple of nuisance parameters is enough for our method to yield the values $\,\sigma _8 = 0.84^{+0.28}_{-0.65}\,$ or σ8 < 1.45 at a 2σ confidence level. This result is consistent with mock simulations and it is also in agreement with independent measurements and presents the first ever measurement of σ8 using SNeIa magnitudes alone.