BATSE Bursts Research Articles

Different Luminosity Correlation of GRBs

We report our recent understanding about a tight correlation between relative spectral lag and luminosity (or redshift) for γ-ray bursts. The latest investigations indicate that the empirical correlations got from BATSE bursts also exist for Swift/BAT ones. The special luminosity-lag correlation is much similar to that of the luminosity with pulse number proposed by Schaefer (2003), but largely different from most of others ever discovered. Note that our newly built luminosity-lag correlation predicts that luminosity should evolve with cosmological redshift as L p ∝ (1 + z)2.4±0.7 that is excellently confirmed by Salvaterra et al. (2012) and Geng & Huang (2013). In addition, it is also surprisingly found that the luminosity-lag correlation can account for both long and short Swift/BAT bursts, which might be an evidence of the same radiation mechanism for diverse burst groups.

Autocorrelation analysis of GRBM–Beppo-SAX burst data

An autocorrelation function (ACF) analysis was performed on 17 gamma-ray bursts with known redshift, using data from the GRBM on board Beppo-SAX. When corrected from the cosmic time dilation effect, the ACFs show a bimodal distribution at about half-maximum, in agreement with a previous study based on BATSE and Konus burst data. Although the results show more dispersion, the separation between the two classes is highly significant.

Are There Any Redshift >8 Gamma‐Ray Bursts in the BATSE Catalog?

Several luminosity indicators have been found for gamma-ray bursts (GRBs) wherein measurable light-curve and spectral properties are well correlated with the peak luminosity. Several papers have each applied one different luminosity relation to find redshifts for BATSE GRBs and claim to identify specific bursts with -->z > 8. The existence of such high-redshift events is not surprising, as BATSE has enough sensitivity to see them and GRBs are expected out to the redshift of the first star formation. To improve results we used five luminosity relations with updated calibrations to determine redshifts with error bars. Combining these relations, we calculated the redshifts of 36 BATSE GRBs with claimed -->z > 8. Our results include 13 bursts with our derived best redshift -->zbest > 8, which looks promising at first. But the calculated redshift uncertainties are significantly large in these selected cases. With only one exception, all of our bursts have -->z1 ? low ? 9. The one exception (BATSE trigger 2035) is likely a short-duration burst at -->z 4. Our best case for a very high redshift event is BATSE trigger 3142 with -->zbest ? 20 and -->z1 ? low = 8.9; however, we can only say -->z > 4.1 at the 2 ? confidence level. In all, we cannot point toward any one BATSE burst as confidently having -->z > 8. One implication is to greatly weaken prior claims that GRBs have a steeply rising rate density out to high redshifts.

Short Gamma‐Ray Bursts with Extended Emission

The recent association of several short gamma-ray bursts (GRBs) with early type galaxies with low star formation rate demonstrates that short bursts arise from a different progenitor mechanism than long bursts. However, since the duration distributions of the two classes overlap, membership is not always easily established. The picture is complicated by the occasional presence of softer, extended emission lasting tens of seconds after the initial spike-like emission. We show that the fundamental defining characteristic of the short burst class is that the initial spike exhibits negligible spectral evolution at energies above ~ 25 keV. This behavior is nearly ubiquitous for the 260 bursts with T90 < 2 s, where the BATSE TTE data completely included the initial spike. The same signature obtains for one HETE-2 and six Swift/BAT short bursts. Analysis of a small sample of "short" BATSE bursts with the most intense extended emission shows that the same lack of evolution on the pulse timescale obtains for the extended emission. The dynamic range in the ratio of peak intensities, spike : extended, is ~ 10^4. For some bursts, the extended emission is only a factor of 2-5 less intense. A high Lorentz factor, ~ 500-1000, might explain the negligible lags observed in short bursts.

Relation between the hardness ratio and time in the first two seconds for compatible samples of short and long gamma-ray bursts

In this paper, we randomly selected a sample ( sample 1) of long gamma-ray bursts, with its size being the same as that of the short-burst group ( sample 2) ( N = 500), from the Current BATSE Bursts Catalog. We randomly selected a short burst and assigned its T-90 to each of these long bursts. We thus constructed a long-burst sample with both the sample size and the distribution of T-90 being the same as those of the short-burst sample obtained from the Current BATSE Bursts Catalog. Then we calculated the hardness ratio (hr(t)) over the assigned T-90 for the long bursts and over their own T90 for the short bursts, and studied the relation between the hardness ratio and the corresponding T90 for these two samples. We also calculated the hardness ratio ( hrt) over the randomly selected 64-ms time intervals within the T-90, and investigated the relation between this hardness ratio and the selected 64-ms time interval. In addition, the hr(T) within and beyond the first two seconds for all the long bursts ( sample 3; N = 1541) were also investigated. We found that the Kolmogorov - Smirnov (KS) probabilities of the distributions of hr(T) (7.15337E- 15) and hr(t) (9.54833E- 10) for samples 1 and 2 are very small, and the average values of hrT and hrt for short bursts are obviously larger than those of the long bursts. The correlations between log hr(T) and log T-90, and between log hrt and log t, are different for samples 1 and 2. These show that short and long bursts in the first two seconds have different characters and they probably originate from different progenitors. For sample 3, for the two time intervals, the KS probability is 5.35828E-5, which suggests that the hardness ratios in different time intervals for long bursts are also different.

Comparison of Redshift‐known Gamma‐Ray Bursts with the Main Groups of Bright BATSE Events

The small reference sample of six BATSE gamma-ray bursts with known redshifts from optical afterglows is compared with a comparison group of the 218 brightest BATSE bursts. These two groups are shown to be consistent both with respect to the distributions of the spectral peak parameter in the observer's frame and also with respect to the distributions of the frame-independent cosmological invariant parameter (CIP). Using the known values of the redshifts (z) for the reference sample, the rest-frame distribution of spectral parameters is built. The deredshifted distribution of the spectral parameters of the reference sample is compared with distribution of these parameters for the comparison group after deredshifting by the factor 1/(1 + z), with z a free parameter. Requiring consistency between these two distributions produces a collective estimation of the best fitting redshifts for the comparison group z = 1.8-3.6. These values can be considered as the average cosmological redshift of the sources of the brightest BATSE bursts. The most probable value of the peak energy of the spectrum in the rest frame is 920 keV.

BATSE Gamma‐Ray Burst Line Search. V. Probability of Detecting a Line in a Burst

The physical importance of the apparent discrepancy between the detections by pre-BATSE missions of absorption lines in gamma-ray burst spectra and the absence of a BATSE line detection necessitates a statistical analysis of this discrepancy. This analysis requires a calculation of the probability that a line, if present, will be detected in a given burst. However, the connection between the detectability of a line in a spectrum and in a burst requires a model for the occurrence of a line within a burst. We have developed the necessary weighting for the line detection probability for each spectrum spanning the burst. The resulting calculations require a description of each spectrum in the BATSE database. With these tools, we identify the bursts in which lines are most likely to be detected. Also, by assuming a small frequency with which lines occur, we calculate the approximate number of BATSE bursts in which lines of various types could be detected. Lines similar to the Ginga detections can be detected in relatively few BATSE bursts; for example, in only ~20 bursts are lines similar to the GB 880205 pair of lines detectable. Ginga reported lines at ~20 and ~40 keV, whereas the low-energy cutoff of the BATSE spectra is typically above 20 keV; hence BATSE's sensitivity to lines is less than that of Ginga below 40 keV, and greater above. Therefore, the probability that the GB 880205 lines would be detected in a Ginga burst rather than a BATSE burst is ~0.2. Finally, we adopt a more appropriate test of the significance of a line feature.

A Search for TeV Counterparts to BATSE Gamma‐Ray Bursts

Intense effort has gone into the observation of optical, radio, and X-ray gamma-ray burst (GRB) counterparts, either simultaneous to the burst or as quasi-steady lingering remnants. Here we report on a similar study at higher energies of 250 GeV and above using ground-based telescopes. The recent technical advances represented by the atmospheric Cherenkov imaging technique (Cawley & Weekes 1995) have opened up the field of gamma-ray astronomy above 250 GeV and raised the possibility that these techniques can be used with excellent fluence sensitivity in exploring the GRB phenomenon. Observations by the Whipple collaboration of nine BATSE positions, one acquired within 2 minutes of the reported BATSE burst time, using coordinates distributed through the BATSE Coordinates Distribution Network (BACODINE) are reported. No evidence of TeV emission is found, and upper limits to the high-energy delayed or extended emission of observed candidates are calculated.

GRBs in the 5-10 keV regime as seen by BATSE

The BATSE Spectroscopy Detectors (SDs) each have a thin beryllium window which allows detection of photons down to 5 keV. Until recently, exploitation of low-energy SD discriminator data has been held up by a lack of knowledge of its energy coverage. A recent series of in-flight changes in the instrument configuration has allowed the SD discriminators to be calibrated fairly accurately. With this in hand, we have surveyed BATSE GRBs for such features as X-ray precursors and after-glows in the roughly 5-10 keV band. For a large percentage of the BATSE bursts, no emission is observed in this energy band. In some cases where low-energy emission has been observed, ratios of flux histories indicate differential spectral evolution. In addition, we have obtained joint spectral fits between the low-energy data and the regular SD data, showing that a single spectral model is sometimes insufficient to fit the entire energy range.

Inferring the spatial and energy distribution of gamma-ray burst sources. 1: Methodology

We describe Bayesian methods for analyzing the distribution of gamma- ray burst peak photon fluxes and directions. These methods fit the differential distribution, and have the following advantages over rival methods: (1) they do not destroy information by binning or averaging the data (as do, say, x^2^, &lt;V/V_max_&gt;, and angular moment analyses); (2) they straightforwardly handle uncertainties in the measured quantities; (3) they analyze the strength and direction information jointly; (4) they use information available about nondetections; and (5) they automatically identify and account for biases and selection effects given a precise description of the experiment. In these methods, the most important information needed about the instrument threshold is not its value at the times of burst triggers, as is used in &lt;V/V_max_&gt; analyses, but rather the value of the threshold at times when no trigger occurred. We show that this information can be summarized as an average detection efficiency that is similar to the product of the exposure and efficiency reported in the First BATSE Burst (1B) Catalog, but significantly different from it at low fluxes. We also quantify an important bias that results from estimating the peak flux by scanning the burst to find the peak number of counts in a window of specified duration, as was done for the LB Catalog. When the duration of the peak of the light curve is longer than the window duration, a simple flux estimate based on the peak counts significantly overestimates the peak flux in a nonlinear fashion that distorts the shape of the log (N)-log (P) distribution. This distortion also corrupts analyses of the V/V_max_ distribution that use ratios of counts above background to estimate V/V_max_. The Bayesian calculation specifies how to account for this bias. Implementation of the Bayesian approach requires some changes in the way burst data are reported that we describe in detail. Subsequent papers will report analyses of the 1B Catalog data using the methods described here.