J/ApJ/919/37 GRB pulse light curve analysis from BATSE (Hakkila, 2021)
How temporal symmetry defines morphology in BATSE gamma-ray burst pulse light
curves.
Hakkila J.
<Astrophys. J., 919, 37 (2021)>
=2021ApJ...919...37H 2021ApJ...919...37H
ADC_Keywords: GRB; Models
Keywords: Gamma-ray bursts ; Light curve classification ;
Astrostatistics techniques
Abstract:
We present compelling evidence that most gamma-ray burst (GRB) pulse
light curves can be characterized by a smooth single-peaked component
coupled with a more complex emission structure that is temporally
symmetric around the time of the pulse peak. The model successfully
fits 86% of Burst and Transient Source Experiment GRB pulses bright
enough for structural properties to be measured. Surprisingly, a GRB
pulse's light-curve morphology can be accurately predicted by the
pulse asymmetry and the stretching/compression needed to align the
structural components preceding the temporal mirror with the time-
reversed components following it. Such a prediction is only possible
because GRB pulses exhibit temporal symmetry. Time-asymmetric pulses
include fast rise exponential decays, rollercoaster pulses, and
asymmetric u-pulses, while time-symmetric pulses include u-pulses and
crowns. Each morphological type is characterized by specific
asymmetries, stretching parameters, durations, and alignments between
the smooth and structured components, and a delineation in the
asymmetry/stretching distribution suggests that symmetric pulses and
asymmetric pulses may belong to separate populations. Furthermore,
pulses belonging to the short GRB class exhibit similar morphologies
to the long GRB class, but appear to simply occur on shorter
timescales.
Description:
To perform this study, we use GRB observations from BATSE at NASA's
Compton Gamma Ray Observatory (Fishman G. J. 2013 EAS Publ. Ser. 61 5).
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table2.dat 106 413 BATSE GRB time-reversed pulse and morphology catalog
table3.dat 151 298 Additional BATSE GRB pulse properties
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See also:
IX/20 : The Fourth BATSE Burst Revised Catalog (Paciesas+ 1999)
J/ApJ/740/104 : BATSE GRB pulse catalog - preliminary data (Hakkila+, 2011)
J/ApJ/744/141 : Shapes of GRB light curves (Bhat+, 2012)
J/ApJ/748/134 : Variability components in BATSE GRB light curves (Gao+, 2012)
J/ApJ/855/101 : BATSE TTE GRB pulse catalog (Hakkila+, 2018)
http://heasarc.gsfc.nasa.gov/docs/cgro/batse/ : BATSE on Heasarc
Byte-by-byte Description of file: table2.dat
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Bytes Format Units Label Explanations
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1- 4 I4 --- TrigNo [105/7575] BATSE trigger number
5- 6 A2 --- m_TrigNo [p1-p3 ] Pulse name within BATSE trigger number
8- 12 F5.3 s Res [0.004/0.064] Temporal resolution; 4ms or 64ms
14- 15 I2 --- Qual [-1/2]? Pulse quality (1)
17- 32 A16 --- Morph GRB pulse morphology; see Section 3.4
34- 38 F5.1 --- S/N [0.2/178]? Signal-to-noise ratio (2)
40- 44 F5.2 --- R [0.3/72]? Residual statistic (3)
46- 50 F5.3 --- CCFRatio [0/1.01]? Cross correlation function ratio
(=CCFresids/CCFmodel; see Appendix)
52- 58 F7.3 --- kappa [-99/1]? Monotonic pulse asymmetry (4)
60- 66 F7.3 --- e_kappa [-99/1]? Uncertainty in kappa (4)
68- 71 F4.2 --- Smirror [0.05/2.64]? Residual stretching parameter
73- 76 F4.2 --- e_Smirror [0/0.4]? Uncertainty in Smirror
78- 83 F6.2 --- Offset [-99/0.65]? Offset (4)
85- 90 F6.2 --- e_Offset [-99/1]? Uncertainty in Offset (4)
92- 98 F7.2 s Start [-278.8/333.1]? Pulse start time
100-106 F7.2 s End [-251.1/514.4]? Pulse end time
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Note (1): Pulse quality code as follows:
-1 = incomplete data;
0 = not temporally symmetric;
1 = monotonic;
2 = temporally symmetric.
Note (2): We define S/N (Hakkila+ 2018ApJ...863...77H 2018ApJ...863...77H) as
Equation (A1): S/N=(Pt-B)/Pt0.5
where Pt is the peak counts measured on the t-millisecond timescale
(t=64 for BATSE 64ms data and t=4 for binned BATSE TTE data) and B is
the mean background count measured on the same timescale.
Note (3): We introduce the residual statistic R to characterize residual
structure brightness, where Equation (16):
R=(σres/)0.5
Here, is the average number of background counts per bin over the
duration of the pulse. See Section 3.1.
Note (4): The asymmetries and offsets (and their errors) of No-pulse
u-pulses are set to -99 to indicate that they cannot be measured.
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Byte-by-byte Description of file: table3.dat
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Bytes Format Units Label Explanations
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1- 4 I4 --- TrigNo [105/7575] BATSE trigger number
5- 6 A2 --- m_TrigNo [p1-p3 ] Pulse name within BATSE trigger number
8- 12 F5.3 s Res [0.004/0.064] Temporal resolution; 4ms or 64ms
14- 19 F6.1 ct B [24/1269] Background (1)
21- 23 F3.1 ct e_B [0.3/6] Uncertainty in B
25- 29 F5.2 ct/s BS [-7.4/9.7] Background rate change (1)
31- 34 F4.2 ct/s e_BS [0/5] Uncertainty in BS
36- 42 F7.1 ct A [5/30231]? Monotonic pulse peak (2)
44- 51 F8.1 ct e_A [0.9/404665]? Uncertainty in A
53- 59 F7.2 s ts [-369/260]? Norris pulse start time (2)
61- 68 F8.2 s e_ts [0/45469]? Uncertainty in ts
70- 79 F10.2 s tau1 [0/9410000]? Norris τ1 (2)
81- 91 F11.2 s e_tau1 [0/64700000]? Uncertainty in tau1
93- 97 F5.2 s tau2 [0/50]? Norris tau2 (2)
99-103 F5.2 s e_tau2 [0/3]? Uncertainty in tau2
105-111 F7.2 s taupk [-254/276]? Norris pulse peak time taupeak (2)
113-119 F7.2 s e_taupk [0/3432]? Uncertainty in taupk
121-127 F7.2 s t0 [-251/424]? Gaussian pulse peak time t0 (2)
129-132 F4.2 s e_t0 [0/5]? Uncertainty in t0
134-138 F5.2 s Sigma [0.05/95.2]? Gaussian pulse dispersion (2)
140-143 F4.2 s e_Sigma [0/7]? Uncertainty in Sigma
145-151 F7.2 s t0mir [-265/411] Time of reflection for pulse residuals
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Note (1): All pulse fits include a linear background model characterized by B
and BS and described in Equation 5.
Note (2): Each pulse has been fitted using either the monotonic asymmetric
Norris et al. (2005ApJ...627..324N 2005ApJ...627..324N) pulse model (Equation 1)
or the monotonic symmetric Gaussian pulse model (Equation 5), but
not both. Background and amplitude apply to both models. The fit
parameters of the Norris model are A, ts, tau1, and tau2. When
fitted along with the background, these parameters result in the
corresponding uncertainties e_A, e_ts, e_tau1, and e_tau2. The
peak of the Norris function occurs at time taupk, and the propagated
uncertainties in the value are e_taupk. The fit parameters of the
Gaussian model are A (this variable is C in equation 5), t0, and
Sigma. When fitted along with the background, these parameters
result in the corresponding uncertainties e_A, e_t0, and e_Sigma.
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History:
From electronic version of the journal
(End) Prepared by [AAS], Emmanuelle Perret [CDS] 18-Jan-2023