Astronomy and Astrophysics – Astrophysics – High Energy Astrophysical Phenomena
Scientific paper
2011-04-30
Astronomy and Astrophysics
Astrophysics
High Energy Astrophysical Phenomena
12 pages, 4 figures, accepted for publication in MNRAS
Scientific paper
Several interesting luminosity correlations among gamma-ray burst (GRB) variables have been recently discussed extensively. In this paper, we derive the six luminosity correlations ($\tlag-L$, $V-L$, $\epkk-L$, $\epkk-E_\gamma$, $\trt-L$, $\epkk-E_{\gamma, \mathrm{iso}}$) from the light curves and spectra of the latest 116 long GRBs, including the time lag ($\tlag$) between low and high photon energy light curves, the variability ($V$) of the light curve, the peak energy of the spectrum ($\epkk$), and the minimum rise time ($\trt$) of the peaks. We find that the intrinsic scatter of the $V-L$ correlation is too large and there seems no inherent correlation between the two parameters using the latest GRB data. The other five correlations indeed exist when the sample is enlarged. The $\epkk-E_\gamma$ correlation has a significantly lower intrinsic scatter compared to the other correlations. We divide the full data into four redshift bins when testing possible evolution of the correlations with redshift. We find no statistically significant evidence for the redshift evolution of the luminosity correlations. To avoid the circularity problem when constraining the cosmological parameters, we simultaneously minimize $\chi^2$ with respect to both correlation parameters $a$, $b$ and the cosmological parameters using the maximum likelihood method. For the flat $\Lambda$CDM, the best fit is $\omm=0.31^{+0.13}_{-0.10}$. We also constrain the possible evolution of the equation of state (EOS) of the dark energy using the GRBs together with the Union2 compilation of SNe Ia and the $H(z)$ data. The result is consistent with the cosmological constant at $2 \sigma$ confidence level and mainly due to the GRB data, the dark energy EOS shows slight deviation from -1 at $z \geq0.5$ as was persistently presented with many previous data sets.
Dai Gao Z.
Qi Shi
Wang Feng-Yu
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