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Weighing Cosmological Models with SNe Ia and GRB Redshift Data
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: Received: 8 April 2019 / Approved: 9 April 2019 / Online: 9 April 2019 (11:38:12 CEST)
A peer-reviewed article of this Preprint also exists.
Gupta, R.P. Weighing Cosmological Models with SNe Ia and Gamma Ray Burst Redshift Data. Universe 2019, 5, 102. Gupta, R.P. Weighing Cosmological Models with SNe Ia and Gamma Ray Burst Redshift Data. Universe 2019, 5, 102.
Abstract
Many models have been proposed to explain the intergalactic redshift using different observational data and different criteria for the goodness-of-fit of a model to the data. The purpose of this paper is to examine several suggested models using the same supernovae Ia data and gamma-ray burst (GRB) data with the same goodness-of-fit criterion and weigh them against the standard CDM model. We have used the redshift – distance modulus ( ) data for 580 supernovae Ia with to determine the parameters for each model, and then use these model parameter to see how each model fits the sole SNe Ia data at and the GRB data up to . For the goodness-of-fit criterion, we have used the chi-square probability determined from the weighted least square sum through non-linear regression fit to the data relative to the values predicted by each model. We find that the standard ΛCDM model gives the highest chi-square probability in all cases albeit with a rather small margin over the next best model - the recently introduced nonadiabatic Einstein de Sitter model. We have made ( ) projections up to for the best four models. The best two models differ in only by 0.328 at , a tiny fraction of the measurement errors that are in the high redshift datasets.
Keywords
galaxies; supernovae; GRB; distances and redshifts; cosmic microwave background radiation; distance scale; cosmology theory; cosmological constant; Hubble constant; general relativity; TMT
Subject
Physical Sciences, Astronomy and Astrophysics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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