English

The Gamma-Ray Burst Hubble Diagram and Its Cosmological Implications

High Energy Astrophysical Phenomena 2015-06-12 v3 Cosmology and Nongalactic Astrophysics

Abstract

In this paper, we continue to build support for the proposal to use gamma-ray bursts (GRBs) as standard candles in constructing the Hubble Diagram at redshifts beyond the current reach of Type Ia supernova observations. We confirm that correlations among certain spectral and lightcurve features can indeed be used as luminosity indicators, and demonstrate from the most up-to-date GRB sample appropriate for this work that the Λ\LambdaCDM model optimized with these data is characterized by parameter values consistent with those in the concordance model. Specifically, we find that (Ωm,ΩΛ)(0.250.06+0.05,0.750.05+0.06)(\Omega_m,\Omega_\Lambda)\approx (0.25_{-0.06}^{+0.05}, 0.75_{-0.05}^{+0.06}), which are consistent, to within 1σ1\sigma, with (0.29,0.71)(0.29,0.71) obtained from the 9-yr WMAP data. We also carry out a comparative analysis between Λ\LambdaCDM and the Rh=ctR_{\rm h}=ct Universe and find that the optimal Λ\LambdaCDM model fits the GRB Hubble Diagram with a reduced χdof22.26\chi^2_{\rm dof}\approx 2.26, whereas the fit using Rh=ctR_{\rm h}=ct results in a χdof22.14\chi^2_{\rm dof}\approx 2.14. In both cases, about 20% of the events lie at least 2σ2\sigma away from the best-fit curves, suggesting that either some contamination by non-standard GRB luminosities is unavoidable, or that the errors and intrinsic scatter associated with the data are being underestimated. With these optimized fits, we use three statistical tools---the Akaike Information Criterion (AIC), the Kullback Information Criterion (KIC), and the Bayes Information Criterion (BIC)---to show that, based on the GRB Hubble Diagram, the likelihood of Rh=ctR_{\rm h}=ct being closer to the correct model is 8596\sim 85-96%, compared to 415\sim 4-15% for Λ\LambdaCDM.

Keywords

Cite

@article{arxiv.1301.0894,
  title  = {The Gamma-Ray Burst Hubble Diagram and Its Cosmological Implications},
  author = {Jun-Jie Wei and Xue-Feng Wu and Fulvio Melia},
  journal= {arXiv preprint arXiv:1301.0894},
  year   = {2015}
}

Comments

39 pages, 7 figures, 2 tables, accepted for publication in ApJ