Spectral hardness evolution characteristics of tracking Gamma-ray Burst pulses

Employing a sample presented by Kaneko et al. (2006) and Kocevski et al. (2003), we select 42 individual tracking pulses (here we defined tracking as the cases in which the hardness follows the same pattern as the flux or count rate time profile) within 36 Gamma-ray Bursts (GRBs) containing 527 time-resolved spectra and investigate the spectral hardness, $E_{peak}$ (where $E_{peak}$ is the maximum of the $\nu F_{\nu}$ spectrum), evolutionary characteristics. The evolution of these pulses follow soft-to-hard-to-soft (the phase of soft-to-hard and hard-to-soft are denoted by rise phase and decay phase, respectively) with time. It is found that the overall characteristics of $E_{peak}$ of our selected sample are: 1) the $E_{peak}$ evolution in the rise phase always start on the high state (the values of $E_{peak}$ are always higher than 50 keV); 2) the spectra of rise phase clearly start at higher energy (the median of $E_{peak}$ are about 300 keV), whereas the spectra of decay phase end at much lower energy (the median of $E_{peak}$ are about 200 keV); 3) the spectra of rise phase are harder than that of the decay phase and the duration of rise phase are much shorter than that of decay phase as well. In other words, for a complete pulse the initial $E_{peak}$ is higher than the final $E_{peak}$ and the duration of initial phase (rise phase) are much shorter than the final phase (decay phase). This results are in good agreement with the predictions of Lu et al. (2007) and current popular view on the production of GRBs. We argue that the spectral evolution of tracking pulses may be relate to both of kinematic and dynamic process even if we currently can not provide further evidences to distinguish which one is dominant. Moreover, our statistical results give some witnesses to constrain the current GRB model.Comment: 32 pages, 26 figures, 3 tables, accepted for publication in New Astronom

Spectral catalogue of bright gamma-ray bursts detected with the BeppoSAX/GRBM

The emission process responsible for the so-called "prompt" emission of gamma-ray bursts is still unknown. A number of empirical models fitting the typical spectrum still lack a satisfactory interpretation. A few GRB spectral catalogues derived from past and present experiments are known in the literature and allow to tackle the issue of spectral properties of gamma-ray bursts on a statistical ground. We extracted and studied the time-integrated photon spectra of the 200 brightest GRBs observed with the Gamma-Ray Burst Monitor which flew aboard the BeppoSAX mission (1996-2002) to provide an independent statistical characterisation of GRB spectra. The spectra were fit with three models: a simple power-law, a cut-off power law or a Band function. The typical photon spectrum of a bright GRB consists of a low-energy index around 1.0 and a peak energy of the nuFnu spectrum E_p~240 keV in agreement with previous results on a sample of bright CGRO/BATSE bursts. Spectra of ~35% of GRBs can be fit with a power-law with a photon index around 2, indicative of peak energies either close to or outside the GRBM energy boundaries. We confirm the correlation between E_p and fluence, with a logarithmic dispersion of 0.13 around the power-law with index 0.21+-0.06. The low-energy and peak energy distributions are not yet explained in the current literature. The capability of measuring time-resolved spectra over a broadband energy range, ensuring precise measurements of parameters such as E_p, will be crucial for future experiments (abridged).Comment: 28 pages, 20 figures, 3 tables, accepted to A&

Spectral lag of gamma-ray burst caused by the intrinsic spectral evolution and the curvature effect

Assuming an intrinsic `Band' shape spectrum and an intrinsic energy-independent emission profile we have investigated the connection between the evolution of the rest-frame spectral parameters and the spectral lags measured in gamma-ray burst (GRB) pulses by using a pulse model. We first focus our attention on the evolution of the peak energy, $E_{0,p}$, and neglect the effect of the curvature effect. It is found that the evolution of $E_{0,p}$ alone can produce the observed lags. When $E_{0,p}$ varies from hard to soft only the positive lags can be observed. The negative lags would occur in the case of $E_{0,p}$ varying from soft to hard. When the evolution of $E_{0,p}$ and the low-energy spectral index $\alpha_{0}$ varying from soft to hard then to soft we can find the aforesaid two sorts of lags. We then examine the combined case of the spectral evolution and the curvature effect of fireball and find the observed spectral lags would increase. A sample including 15 single pulses whose spectral evolution follows hard to soft has been investigated. All the lags of these pulses are positive, which is in good agreement with our theoretical predictions. Our analysis shows that only the intrinsic spectral evolution can produce the spectral lags and the observed lags should be contributed by the intrinsic spectral evolution and the curvature effect. But it is still unclear what cause the spectral evolution.Comment: 10 pages, 7 figure

Continuum spectral evolution of gamma-ray bursts

Gamma-ray bursts (GRBs) remain one of the most inexplicable astrophysical phenomena observed today. While counterparts at other wavelengths would provide the best clues as to the nature of GRBs, none have been observed. To supplement studies on GRB distribution and population statistics, temporal morphologies, and spectral line searches, we focus on the analysis of GRB continuum spectral evolution. Previous spectral evolution studies have shown a variety of patterns: most individual pulses show a hard-to-soft evolution, but studies of both the SIGNE and BATSE GRB databases reveal several other patterns, including hardness-intensity tracking, soft-to-hard, static, and chaotic spectral evolution. This type of analysis attempts to identify spectral evolution signatures that can discriminate between different physical scenarios or different GRB subpopulations based on temporal profile, duration, intensity, or spatial distribution. Contrary to most studies that use only one model and one parameter to characterize spectral evolution, several models are used here. Statistically equivalent models are shown to give consistent physical results. I verify the variety of spectral evolution patterns present in GRBs, and investigate how the actual shape of the spectrum evolves, following multi-parameter spectral fits in time. Different spectral evolution patterns exist simultaneously in multiple parameters. Hardness-intensity correlations in pulse and over burst decay phases are quantitatively examined: correlation is often significant, but the relation between hardness and intensity is non-unique. Hardness-intensity lag-times are found to correlate to the rise-time of the hardness profile. Comparisons of double-pulse GRBs reveals a variety of results, including the implication that late-emitting pulses are less affected by early emission

Analysis of gamma ray burst spectra with cyclotron lines

Motivated by the recent developments in the cyclotron resonance upscattering of soft photons or CUSP model of Gamma Ray Burst (GRB) continuum spectra, we revisit a select database of GRBs with credible cyclotron absorption features. We measure the break energy of the continuum, the slope below the break and deduce the soft photon energy or the electron beam Lorentz factor cutoff. We study the correlation (or lack of) between various parameters in the context of the CUSP model. One surprise result is that there appears to be marginal correlation between the break energy and the spectral index below the break. 20 refs., 8 figs., 2 tabs

Rapid spectral evolution analysis of BATSE GRBs

We analyze the evolution of BATSE GRB continuum spectra using the BATSE Spectral Analysis Software. We check the consistency of various methods used to characterize spectral hardness: hardness ratio, single power law index, OTTB temperature, and break energy of a broken power law fit. Time evolution of spectral parameters is compared with derived energy fluxes. We also search for correlations between the different spectral parameters and derived quantities, such as break energy, power law indices above and below the break, and energy flux