The Identification of Two Different Spectral Types of Pulses in Gamma‐Ray Bursts

Pendleton, G. N.; Pačiesas, W. S.; Briggs, M. S.; Preece, R.; Mallozzi, Robert S.; Meegan, C. A.; Horack, John M.; Fishman, G. J.; Band, D. L.; Matteson, J. L.; Skelton, R. T.; Hakkila, Jon; Ford, L.; Kouveliotou, C.; Koshut, T. M.

doi:10.1086/304763

Cited by 73 publications

(63 citation statements)

References 13 publications

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“…Similarly to Pendleton et al (1997) and Balázs et al (1998) these events have a F 256 peak-flux (i.e. on 256 ms trigger scale) smaller than 0.65 photon/(cm 2 s).…”

Section: Application On Grbs: Estimation Of the Redshiftssupporting

confidence: 60%

Gamma photometric redshifts for long gamma-ray bursts

Bagoly

Csabai

Mészáros

et al. 2003

A&A

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Abstract. It is known that the soft tail of the gamma-ray bursts' spectra show excesses from the exact power-law dependence. In this article we show that this departure can be detected in the peak flux ratios of different BATSE DISCSC energy channels. This effect allows to estimate the redshift of the bright long gamma-ray bursts in the BATSE Catalog. A verification of these redshifts is obtained for the 8 GRB which have both BATSE DISCSC data and measured optical spectroscopic redshifts. There is good correlation between the measured and estimated redshifts, and the average error is ∆z ≈ 0.33. The method is similar to the photometric redshift estimation of galaxies in the optical range, hence it can be called as "gamma photometric redshift estimation". The estimated redshifts for the long bright gamma-ray bursts are up to z 4. For the the faint long bursts -which should be up to z 20 -the redshifts cannot be determined unambiguously with this method.

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“…Similarly to Pendleton et al (1997) and Balázs et al (1998) these events have a F 256 peak-flux (i.e. on 256 ms trigger scale) smaller than 0.65 photon/(cm 2 s).…”

Section: Application On Grbs: Estimation Of the Redshiftssupporting

confidence: 60%

Gamma photometric redshifts for long gamma-ray bursts

Bagoly

Csabai

Mészáros

et al. 2003

A&A

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“…A few algorithms for identification have been introduced by, e.g., Li & Fenimore (1996), Pendleton et al (1997), and Scargle (1998). Norris et al (1996) developed a method to identify pulses based on assuming stretched exponential pulse shapes.…”

Section: Sample Selectionmentioning

confidence: 99%

On the Hardness‐Intensity Correlation in Gamma‐Ray Burst Pulses

Borgonovo

Ryde

2001

ApJ

115

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We study the hardness-intensity correlation (HIC) in gamma-ray bursts (GRBs). In particular, we analyze the decay phase of pulse structures in their light curves. The study comprises a sample of 82 long pulses selected from 66 long bursts observed by the Burst And Transient Source Experiment (BATSE) on the Compton Gamma-Ray Observatory. We find that at least 57% of these pulses have HICs that can be well described by a power law. A number of the other cases can still be explained with the power law model if various limitations of the observations are taken into account.The distribution of the power law indices γ, obtained by modeling the HIC of pulses from different bursts, is broad with a mean of 1.9 and a standard deviation of 0.7. We also compare indices among pulses from the same bursts and find that their distribution is significantly narrower. The probability p of a random coincidence is shown to be very small (< 2 × 10 −5 ). In most cases, the indices are equal to within the uncertainties. These results demand a physical model to be able to reproduce multiple pulses with similar characteristics for an individual burst, but with a large diversity for pulses from an ensemble of bursts. This is particularly relevant when comparing the external versus the internal models.In our analysis, we also use a new method for studying the hardness-intensity correlation, in which the intensity is represented by the peak value of the EF E spectrum, where E is the energy and F E is the energy flux spectrum. We compare it to the traditional method in which the intensity over a finite energy range is used instead, which may be an incorrect measure of the bolometric intensity. This new method gives stronger correlations and is useful in the study of various aspects of the HIC. In particular, it produces a better agreement between indices of different pulses within the same burst. Also, we find that some pulses exhibit a track jump in their HICs, in which the correlation jumps between two power laws with the same index. We discuss the possibility that the track jump is caused by strongly overlapping pulses. Based on our findings, the constancy of the index is proposed to be used as a tool for pulse identification in overlapping pulses and examples of its application are given.

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“…The impressive results from these studies include (1) hard to soft evolution (Golenetskii et al 1983;Borgonovo & Ryde 2001); (2) the duration-hardness anticorrelation (Kouveliotou et al 1993); (3) the temporal asymmetry of pulses in GRBs (Nemiroff et al 1993;Link & Epstein 1996); (4) a bimodal duration distribution of GRBs consistent with two lognormal distributions (Kouveliotou et al 1993;McBreen et al 1994); (5) the discovery of two different types of pulses in GRBs (Pendleton et al 1997); (6) a correlation between E peak and intensity (Mallozzi et al 1995); (7) energy dependence of the pulse duration (Norris et al 1996); (8) a relationship between the pulse peak energy, E peak , and the photon fluence (Liang & Kargatis 1996;Crider et al 1999); (9) lognormal pulse shapes and time intervals between pulses in long Hurleyet al 1998) and short GRBs ); (10) spectra well fit with a Band function (Band et al 1993); (11) spectral hardening before a count rate increase (Bhat et al 1994); (12) an X-ray excess in GRB spectra (Strohmayer et al 1998); (13) a correlation between complexity and brightness (Stern et al 1999) and (14) the unique properties of the pulses and power law relationships between the pulse properties and durations of GRBs (McBreen et al 2002) While GRBs display hard to soft spectral evolution, there is remarkable constancy of the pulses in GRBs throughout the burst (Ramirez-Ruiz & Fenimore 2000;Quilligan et al 1999). The temporal and spectral properties of a few GRBs with known redshift have yielded two important results to suggest that GRB properties may be related to their luminosities.…”

Section: Introductionmentioning

confidence: 99%

Temporal properties of gamma ray bursts as signatures of jets from the central engine

Quilligan

McBreen

Hanlon

et al. 2002

A&A

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Abstract.A comprehensive temporal analysis has been performed on the 319 brightest GRBs with T90 > 2 s from the BATSE current catalog. The GRBs were denoised using wavelets and subjected to an automatic pulse selection algorithm as an objective way of identifying pulses and quantifying the effects of neighbouring pulses. The number of statistically significant pulses selected from the sample was greater than 3000. The rise times, fall times, full-widths at half-maximum (FWHM), pulse amplitudes and pulse areas were measured and the frequency distributions are presented here. All are consistent with lognormal distributions provided the pulses are well separated. The distribution of time intervals between pulses is not random but compatible with a lognormal distribution when allowance was made for the 64 ms time resolution and a small excess (5%) of long duration intervals that is often referred to as a Pareto-Lévy tail. The time intervals between pulses are most important because they may be an almost direct measure of the activity in the central engine. Lognormal distributions of time intervals also occur in pulsars and SGR sources and therefore provide indirect evidence that the time intervals between pulses in GRBs are also generated by rotation powered systems with super-strong magnetic fields. A range of correlations are presented on pulse and burst properties. The rise and fall times, FWHM and area of the pulses are highly correlated with each other. The pulse amplitudes are anticorrelated with the FWHM. The time intervals between pulses and pulse amplitudes of neighbouring pulses are correlated with each other. It was also found that the number of pulses, N, in GRBs is strongly correlated with the fluence and duration and that can explain the well known correlation between duration and fluence. The GRBs were sorted into three categories based on N i.e. 3 ≤ N ≤ 12, 13 ≤ N ≤ 24 and N ≥ 25. The properties of pulses before and after the strongest pulse were compared for three categories of bursts. No major differences were found between the distributions of the pulse properties before and after the strongest pulse in the GRB. However there is a strong trend for pulses to have slower rise times and faster fall times in the first half of the burst and this pattern is strongest for category 3 ≤ N ≤ 12. This analysis revealed that the GRBs with large numbers of pulses have narrower and faster pulses and also larger fluences, longer durations and higher hardness ratios than the GRBs with smaller numbers of pulses. These results may be explained by either homogeneous or inhomogeneous jet models of GRBs. The GRBs with larger number of pulses are closer to the axis if Γ varies with the opening angle of the jet and the imprint of the jet is preserved in the pulse structure of the burst. The distribution of the number of pulses per GRB broadly reflects the beaming by the jet.

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The Identification of Two Different Spectral Types of Pulses in Gamma‐Ray Bursts

Cited by 73 publications

References 13 publications

Gamma photometric redshifts for long gamma-ray bursts

Gamma photometric redshifts for long gamma-ray bursts

On the Hardness‐Intensity Correlation in Gamma‐Ray Burst Pulses

Temporal properties of gamma ray bursts as signatures of jets from the central engine

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