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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.
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 uncertaint
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 minimi
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 ene
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 wh
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 con