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Register a new userProbing Dark Energy with Supernovae : Bias from the time evolution of the equation of state
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Observation of thousands of type Ia supernovae should offer the most direct approach to probe the dark energy content of the universe. This will be undertaken by future large ground-based surveys followed by a space mission (SNAP/JDEM). We address the problem of extracting the cosmological parameters from the future data in a model independent approach, with minimal assumptions on the prior knowledge of some parameters. We concentrate on the comparison between a fiducial model and the fitting function and adress in particular the effect of neglecting (or not) the time evolution of the equation of state. We present a quantitative analysis of the bias which can be introduced by the fitting procedure. Such bias cannot be ignored as soon as the statistical errors from present data are drastically improved.
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The gravitational magnification and demagnification of Type Ia supernovae (SNe) modify their positions on the Hubble diagram, shifting the distance estimates from the underlying luminosity-distance relation. This can introduce a systematic uncertainty in the dark energy equation of state (EOS) estimated from SNe, although this systematic is expected to average away for sufficiently large data sets. Using mock SN samples over the redshift range $0 < z leq 1.7$ we quantify the lensing bias. We find that the bias on the dark energy EOS is less than half a percent for large datasets ($gtrsim$ 2,000 SNe). However, if highly magnified events (SNe deviating by more than 2.5$sigma$) are systematically removed from the analysis, the bias increases to $sim$ 0.8%. Given that the EOS parameters measured from such a sample have a 1$sigma$ uncertainty of 10%, the systematic bias related to lensing in SN data out to $z sim 1.7$ can be safely ignored in future cosmological measurements.
We develop an efficient, non-parametric Bayesian method for reconstructing the time evolution of the dark energy equation of state w(z) from observational data. Of particular importance is the choice of prior, which must be chosen carefully to minimise variance and bias in the reconstruction. Using a principal component analysis, we show how a correlated prior can be used to create a smooth reconstruction and also avoid bias in the mean behaviour of w(z). We test our method using Wiener reconstructions based on Fisher matrix projections, and also against more realistic MCMC analyses of simulated data sets for Planck and a future space-based dark energy mission. While the accuracy of our reconstruction depends on the smoothness of the assumed w(z), the relative error for typical dark energy models is <10% out to redshift z=1.5.
In this letter we propose a test to detect the linearity of the dark energy equation of state, and apply it to two different Type Ia Supernova (SN Ia) data sets, Union2.1 and SNLS3. We find that: a. current SN Ia data are well described by a dark energy equation of state linear in the cosmic scale factor a, at least up to a redshift z = 1, independent of the pivot points chosen for the linear relation; b. there is no significant evidence of any deviation from linearity. This apparent linearity may reflect the limit of dark energy information extractable from current SN Ia data.
Superluminous supernovae (SLSNe) are luminous transients that can be detected to high redshifts with upcoming optical time-domain surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). An interesting open question is whether the properties of SLSNe evolve through cosmic time. To address this question, in this paper we model the multi-color light curves of all 21 Type I SLSNe from the Dark Energy Survey (DES) with a magnetar spin-down engine, implemented in the Modular Open Source Fitter for Transients (MOSFiT). With redshifts up to $zapprox 2$, this sample includes some of the highest-redshift SLSNe. We find that the DES SLSNe span a similar range of ejecta and magnetar engine parameters to previous samples of mostly lower-redshift SLSNe (spin period $Papprox 0.79-13.61$ ms, magnetic field $Bapprox (0.03-7.33)times10^{14}$ G, ejecta mass $M_{rm ej}approx 1.54-30.32$ M$_{odot}$, and ejecta velocity $v_{rm ej}approx (0.55-1.45)times 10^4$ km s$^{-1}$). The DES SLSN sample by itself exhibits the previously found negative correlation between $M_{rm ej}$ and $P$, with a pronounced absence of SLSNe with low ejecta mass and rapid spin. Combining our results for the DES SLSNe with 60 previous SLSNe modeled in the same way, we find no evidence for redshift evolution in any of the key physical parameters.
Are geometrical summaries of the CMB and LSS sufficient for estimating cosmological parameters? And how does our choice of a dark energy model impact the current constraints on standard cosmological parameters? We address these questions in the context of the widely used CPL parametrization of a time varying equation of state w in a cosmology allowing spatial curvature. We study examples of different behavior allowed in a CPL parametrization in a phase diagram, and relate these to effects on the observables. We examine parameter constraints in such a cosmology by combining WMAP5, SDSS, SNe, HST data sets by comparing the power spectra. We carefully quantify the differences of these constraints to those obtained by using geometrical summaries for the same data sets. We find that (a) using summary parameters instead of the full data sets give parameter constraints that are similar, but with discernible differences, (b) due to degeneracies, the constraints on the standard parameters broaden significantly for the same data sets. In particular, we find that in the context of CPL dark energy, (i) a Harrison-Zeldovich spectrum cannot be ruled out at $2sigma$ levels with our current data sets. and (ii) the SNe IA, HST, and WMAP 5 data are not sufficient to constrain spatial curvature; we additionally require the SDSS DR4 data to achieve this.
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