An unexpectedly low-redshift excess of Swift gamma-ray burst rate

H. Yu; F. Y. Wang; Z. G. Dai; K. S. Cheng

doi:10.1088/0067-0049/218/1/13

paper

An unexpectedly low-redshift excess of Swift gamma-ray burst rate

arXiv:1504.01812 · doi:10.1088/0067-0049/218/1/13

Abstract

Gamma-ray bursts (GRBs) are the most violent explosions in the Universe and can be used to explore the properties of high-redshift universe. It is believed that the long GRBs are associated with the deaths of massive stars. So it is possible to use GRBs to investigate the star formation rate (SFR). In this paper, we use Lynden-Bell's $c^{-}$ method to study the luminosity function and rate of \emph{Swift} long GRBs without any assumptions. We find that the luminosity of GRBs evolves with redshift as $L (z) \propto g (z) = (1 + z)^{k}$ with $k = 2.4 3_{- 0.38}^{+ 0.41}$ . After correcting the redshift evolution through $L_{0} (z) = L (z) / g (z)$ , the luminosity function can be expressed as $ψ (L_{0}) \propto L_{0}^{- 0.14 \pm 0.02}$ for dim GRBs and $ψ (L_{0}) \propto L_{0}^{- 0.70 \pm 0.03}$ for bright GRBs, with the break point $L_{0}^{b} = 1.43 \times 1 0^{51} erg s^{- 1}$ . We also find that the formation rate of GRBs is almost constant at $z < 1.0$ for the first time, which is remarkably different from the SFR. At $z > 1.0$ , the formation rate of GRB is consistent with the SFR. Our results are dramatically different from previous studies. Some possible reasons for this low-redshift excess are discussed. We also test the robustness of our results with Monte Carlo simulations. The distributions of mock data (i.e., luminosity-redshift distribution, luminosity function, cumulative distribution and $lo g N - lo g S$ distribution) are in good agreement with the observations. Besides, we also find that there are remarkable difference between the mock data and the observations if long GRB are unbiased tracers of SFR at $z < 1.0$ .

33 pages, 10 figures, 1 table, accepted by ApJS

An unexpectedly low-redshift excess of Swift gamma-ray burst rate

Abstract

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