Modified Statistical Analysis of SNe1a Data
Department of Physics, National University of Singapore,, 2 Science Drive 3, Singapore 117542
Published online: 21 August 2020
We review an improved maximum likelihood analysis of the Type 1a Supernova (SNe1a) data. We calculate the profile likelihood in the Ωm -ΩΛ pa- rameter space by conducting a parameter sweep across the 8 SNe1a parameters, using a Markov Chain Monte Carlo (MCMC) optimization algorithm. This im- proved analysis, which does not assume arbitrary values for the uncertainties, has the advantage of being bias-free as compared to the original analysis. We use the Joint Lightcurve Analysis (JLA) dataset containing 740 SN1a data sam- ples for our study, and compare among 5 different models: the ΛCDM model, the flat wCDM model, its non-flat generalization, as well as two dynamical w(z) parametrizations. We find that the ΛCDM model is favoured over the other models, and the best fit values based on this model are Ωm =0.40 and ΩΛ =0.55. Interestingly, in most of the contour plots we obtain, the line of no acceleration is crossed at 2∼3σ confidence levels, which is similar to the results published by Nielsen et al, the original authors who introduced the improved maximum like- lihood analysis. When we generalize the wCDM model to the dynamical w(z) parametrizations, the evidence for cosmic acceleration becomes even weaker. This raises the question of how secure we can be of an accelerating expansion of the universe.
© The Authors, published by EDP Sciences, 2020
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