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Abstract: We study characters of recent type Ia supernova SNIa data using evolvingdark energy models with changing equation of state parameter w. We considersudden-jump approximation of w for some chosen redshift spans with doubletransitions, and constrain these models based on Markov Chain Monte CarloMCMC method using the SNIa data Constitution, Union, Union2 together withbaryon acoustic oscillation A parameter and cosmic microwave background shiftparameter in a flat background. In the double-transition model the Constitutiondata shows deviation outside 1 sigma from LCDM model at low z < 0.2 andmiddle 0.2 < z < 0.4 redshift bins whereas no such deviations are noticeablein the Union and Union2 data. By analyzing the Union members in theConstitution set, however, we show that the same difference is actually due todifferent calibration of the same Union sample in the Constitution set, and isnot due to new data added in the Constitution set. All detected deviations arewithin 2 sigma from the LCDM world model. From the LCDM mock data analysis, wequantify biases in the dark energy equation of state parameters induced byinsufficient data with inhomogeneous distribution of data points in theredshift space and distance modulus errors. We demonstrate that location ofpeak in the distribution of arithmetic means computed from the MCMC chain foreach mock data behaves as an unbiased estimator for the average bias, which isvalid even for non-symmetric likelihood distributions.



Author: Jaehong Park, Chan-Gyung Park, Jai-chan Hwang

Source: https://arxiv.org/







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