Energetics–spectral correlations versus the BATSE gamma-ray bursts population

The proposed correlations between the energetics of gamma-ray bursts (GRBs) and their spectral properties, namely the peak energy of their prompt emission, can broadly account for the observed fluence distribution of all 'bright' BATSE GRBs, under the hypothesis that the GRB rate is proportional to the star formation rate and that the observed distribution in peak energy is independent of redshift. The correlations can also be broadly consistent with the properties of the whole BATSE long GRB population for a peak energy distribution smoothly extending towards lower energies, and in agreement with the properties of a sample at 'intermediate' fluences and with the luminosity functions inferred from the GRB number counts. We discuss the constraints that this analysis imposes on the shape of such peak energy distribution, the opening angle distribution and the tightness of the proposed correlations.     

A Test on Different Types of the Time Curve of Hardness Ratio of Gamma-Ray Bursts based on the Curvature Effect

We analyzed a sample of 66 gamma-ray bursts (GRBs) and statistically confirmed the prediction on the time curve of the hardness ratio of GRBs made by Qin et al. based on the curvature effect. In their analysis, GRB pulses are divided into three types according to the shape of their raw hardness ratio (RHR) time curves, defined as to include the background counts to the signal counts, so as to make use of counts within small time intervals. Of the three types, very hard sources exhibit a perfect pulse-like profile (type 1), hard bursts possess a pulse-like profile with a dip in the decay phase (type 2), and soft bursts show no pulse-like profile but have only a dipped profile (type 3). In terms of the conventional hardness ratio, type 3 sources are indeed generally softer than those of type 1 and type 2, in agreement with the prediction. We found that the minimum value of RHR is sensitive in distinguishing the different types. We propose that GRB pulses can be classified according to the minimum value of RHR and that the different type sources may be connected with different strengths of the shock or/and the magnetic field.     

Multiple regression of GRB luminosity on light-curve properties

Statistical relations between the luminosity L of gamma-ray burster (GRBs) and several of their other observable properties have been discovered. Four of these properties are considered here: the spectral lag; a measure of the light-curve variability; peak energy and the minimum light-curve rise time. Data are taken from a tabulation by Schaefer. Log–log regression of L on various combinations of the four light-curve properties, as well as redshift, is considered, using conventional multiple linear regression, and multiple errors-in-variables regression. Several cases are found in which the regression coefficients of more than one luminosity indicators are significant. In particular, the simultaneous regression of luminosity on peak energy, spectral lag, minimum rise time and redshift is meaningful.     

Precursor activity in bright, long BATSE gamma-ray bursts

We study a sample of bright, long Burst and Transient Source Experiment (BATSE) gamma-ray burst (GRB) light curves in the 200 s before the detection of the GRB prompt emission. We find that in a sizable fraction of cases (∼20 per cent) there is evidence of emission above the background coming from the same direction as the GRB. This emission is characterized by a softer spectrum with respect to the main spectrum and contains a small fraction (0.1–1 per cent) of the total event counts. The precursors have typical delays of several tens of seconds extending (in few cases) up to 200 s (the limit of the investigated period). Their spectra are typically non-thermal power-law spectra, aside from a few cases. Such long delays and the non-thermal origin of their spectra are hard to reconcile with any model for the progenitor.     

PSR J0737–3039A: baseband timing and polarimetry

In the present paper, we investigate the distribution of the hardness ratio (HR) for short and long gamma-ray bursts (GRBs) in different time-scales for the first two seconds. After including and subtracting the background count, we performed a Kolmogorov–Smirnov (K–S) test on the HR distributions of the two classes of GRBs in each time interval. Our analysis shows that the probabilities of the K–S test to the distributions are very small, suggesting that the two classes of bursts are unlikely to arise from the same HR distributions. The result indicates that the two kinds of bursts probably originate from different mechanisms or have different central engines. In addition, we found that the HR of short bursts within the time interval 0–0.96 s changes from hard to soft; the HR of long bursts does not. The two kinds of bursts have different characteristics in the first two seconds, which might be associated with different physical mechanisms.     

Confidence intervals for the correlation between the gamma-ray burst peak energy and the associated supernova peak brightness

A strong correlation between the gamma-ray burster peak energy and the peak luminosity of the associated supernova was discovered by Li for four GRBs. Despite the fact that the formal significance level of the correlation is 0.3 per cent, the smallness of the data set requires careful further evaluation of the result. Subject to the assumption that the data are bivariate Gaussian, a 95 per cent confidence interval of  (−0.9972, 0.02)  for the correlation is derived. Using data from the literature, it is shown that the distribution of known peak GRB energies is not Gaussian if X-ray flashes are included in the sample. This leads to a proposed alternative to the bivariate Gaussian model, which entails describing the dependence between the two variables by a Gaussian copula. The copula is still characterized by a correlation coefficient. The Bayesian posterior distribution of the correlation coefficient is evaluated using a Markov chain Monte Carlo method. The mean values of the posterior distributions range from −0.33 to about zero, depending on the specifics of the supernova (SN) peak brightness distribution. The implication is that the existing data favour a modest correlation between the GRB peak energy and the SN peak brightness; confidence intervals are very wide and include zero.     

A Close Correlation between the Spectral Lags and Redshifts of Gamma-Ray Bursts

Based on nine BATSE GRBs with known redshifts, we found that the maximum spectral lag of all the pulses in a gamma-ray burst （GRB） appears to be anti-correlated with the redshift of the burst. In order to confirm this finding, we analyzed 10 GRBs detected by HETE-2 with known redshifts and found a similar relation. Using the relation, we estimated the redshifts of 878 long GRBs in the BATSE catalog, then we investigated the distributions of the redshifts and 869 Eiso of these GRBs. The distribution of the estimated redshifts is concentrated at z = 1.4 and the distribution of Eiso peaks at 10^52.5 erg. The underlying physics of the correlation is unclear at present.     

Gamma-Ray Bursts in the Swift Era

1 INTRODUCTION Gamma-ray bursts (GRBs) are fascinating celestial objects. These short, energetic bursts of gamma-rays mark the most violent, cataclysmic explosions in the universe, likely associated with the births of stellar- size black holes or rapidly spinning, highly magnetized neutron stars. Since the detections of their long- wavelength afterglows (Costa et al. 1997; van Paradijs et al. 1997; Frail et al. 1997), GRBs are observa- tionally accessible in essentially all electromagn…