Find in Library

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 Lambda cold dark matter model (&#923;CDM). We have used the redshift&#8212;distance modulus (<i>z</i> &#8722; <i>&#956;</i>) data for 580 supernovae Ia with 0.015 &#8804; <i>z</i> &#8804; 1.414 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 <i>z</i> = 1.914 and the GRB data up to <i>z</i> = 8.1. 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 &#923;CDM model gives the highest chi-square probability in all cases albeit with a rather small margin over the next best model&#8212;the recently introduced nonadiabatic Einstein de Sitter model. We have made (<i>z</i> &#8722; <i>&#956;</i>) projections up to <i>z</i> = 1096 for the best four models. The best two models differ in <i>&#956;</i> only by 0.328 at <i>z</i> = 1096, a tiny fraction of the measurement errors that are in the high redshift datasets.