J/ApJ/903/33 1366 LGRB redshifts estimates with BARSE (Osborne+, 2020)
A Multilevel Empirical Bayesian Approach to Estimating the Unknown Redshifts of
1366 BATSE Catalog Long-duration Gamma-Ray Bursts.
Osborne, Osborne J.A., Shahmoradi A., Nemiroff R.J.
<Astrophys. J., 903, 33 (2020)>
=2020ApJ...903...33O 2020ApJ...903...33O
ADC_Keywords:GRB; Redshifts
Keywords: Gamma-ray bursts ; Gamma-ray detectors ; Astronomy data
modeling ; Astronomy data analysis ; Luminosity function ;
Catalogs ; Markov chain Monte Carlo ; Astronomical simulations
; Bayesian statistics ; Hierarchical models
Abstract:
We present a catalog of probabilistic redshift estimates for 1366
individual Long-duration Gamma-ray Bursts (LGRBs) detected by the
Burst And Transient Source Experiment (BATSE). This result is based on
a careful selection and modeling of the population distribution of
1366 BATSE LGRBs in the five-dimensional space of redshift and the
four intrinsic prompt gamma-ray emission properties: the isotropic
1024ms peak luminosity (Liso), the total isotropic emission
(Eiso), the spectral peak energy (Epz), as well as the intrinsic
duration (T90z), while carefully taking into account the effects of
sample incompleteness and the LGRB-detection mechanism of BATSE. Two
fundamental plausible assumptions underlie our purely probabilistic
approach: (1) LGRBs trace, either exactly or closely, the cosmic star
formation rate, with a possibility of the excess rates of LGRBs in the
nearby universe, and (2) the joint four-dimensional distribution of
the aforementioned prompt gamma-ray emission properties is well
described by a multivariate log-normal distribution. Our modeling
approach enables us to constrain the redshifts of individual BATSE
LGRBs to within 0.36 and 0.96 average uncertainty ranges at 50% and
90% confidence levels, respectively. Our redshift predictions are
completely at odds with the previous redshift estimates of BATSE LGRBs
that were computed via the proposed phenomenological high-energy
relations, specifically, the apparently strong correlation of LGRBs'
peak luminosity with the spectral peak energy, lightcurve variability,
and spectral lag. The observed discrepancies between our predictions
and the previous works can be explained by the strong influence of
detector threshold and sample incompleteness in shaping these
phenomenologically proposed high-energy correlations in the
literature. Finally, we also discuss the potential effects of an
excess cosmic rate of LGRBs at low redshifts and the possibility of a
luminosity evolution of LGRBs on our results.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table2.dat 65 1366 BATSE 1366 LGRB redshift estimates
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See also:
IX/20 : The Fourth BATSE Burst Revised Catalog (Paciesas+ 1999)
J/ApJ/763/15 : Fermi GRB analysis. III. T90 distributions (Qin+, 2013)
J/ApJS/208/21 : The BATSE 5B GRB spectral catalog (Goldstein+, 2013)
J/ApJ/829/7 : 3rd Swift/BAT GRB catalog (past ∼11yrs) (BAT3) (Lien+, 2016)
J/ApJ/850/161 : Konus-Wind cat. of GRBs with redshifts. I. (Tsvetkova+, 2017)
Byte-by-byte Description of file: table2.dat
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Bytes Format Units Label Explanations
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1- 4 I4 --- ID [105/8121] BATSE trigger identifier
6- 9 F4.2 --- H06 [0.22/3.94] Hopkins & Beacom, 2006ApJ...651..142H 2006ApJ...651..142H
model mean redshift
11- 14 F4.2 --- b_H06 [0.1/2.4] Lower 90% prediction interval boundary
in H06
16- 19 F4.2 --- B_H06 [0.4/5.5] Upper 90% prediction interval boundary
in H06
21- 24 F4.2 --- B10 [0.27/8.57] Butler+, 2010, J/ApJ/711/495 model
mean redshift
26- 29 F4.2 --- b_B10 [0.1/6.7] Lower 90% prediction interval boundary
in B10
31- 35 F5.2 --- B_B10 [0.4/11] Upper 90% prediction interval boundary in
B10
37- 40 F4.2 --- M17 [0.2/5.31] Madau & Fragos, 2017ApJ...840...39M 2017ApJ...840...39M
model mean redshift
42- 45 F4.2 --- b_M17 [0.1/4.3] Lower 90% prediction interval boundary
in M17
47- 50 F4.2 --- B_M17 [0.2/6.4] Upper 90% prediction interval boundary
in M17
52- 55 F4.2 --- P15 [0.17/4.57] Petrosian+, 2015ApJ...806...44P 2015ApJ...806...44P model
mean redshift
57- 60 F4.2 --- b_P15 [0.1/3.7] Lower 90% prediction interval boundary
in P15
62- 65 F4.2 --- B_P15 [0.2/5.4] Upper 90% prediction interval boundary
in P15
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History:
From electronic version of the journal
(End) Prepared by [AAS], Coralie Fix [CDS], 26-Jan-2022