Analysis of recent type Ia supernova data based on evolving dark energy models

We study characters of recent type Ia supernova data using evolving dark energy models with changing equation-of-state parameter w. We consider a sudden-jump approximation of w for some chosen redshift spans with double transitions and constrain these models based on the Markov chain Monte Carlo method using the type Ia supernova data (Constitution, Union, Union2), together with the baryon acoustic oscillation A parameter and the cosmic microwave background shift parameter in a flat background. In the double-transition model, the Constitution data shows deviation outside 1σ from the Λ cold dark matter (ΛCDM) model at low (z0.2) and middle (0.2 0.4) redshift bins, whereas no such deviations are noticeable in the Union and Union2 data. By analyzing the Union members in the Constitution set, however, we show that the same difference is actually due to different calibration of the same Union sample in the Constitution set and is not due to new data added in the Constitution set. All detected deviations are within 2σ from the ΛCDM world model. From the ΛCDM mock data analysis, we quantify biases in the dark energy equation-of-state parameters induced by insufficient data with inhomogeneous distribution of data points in the redshift space and distance modulus errors. We demonstrate that the location of the peak in the distribution of arithmetic means (computed from the Markov chain Monte Carlo chain for each mock data) behaves as an unbiased estimator for the average bias, which is valid even for nonsymmetric likelihood distributions.