A Comprehensive Statistical Study of Gamma-Ray Bursts

Wang, Feifei; Zou, Yuan‐Chuan; Liu, Fuxiang; Liao, Bin; Liu, Yu; Chai, Yating; Xia, L.

doi:10.3847/1538-4357/ab0a86

Cited by 37 publications

(27 citation statements)

References 562 publications

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“…In L19, the authors use a data set taken from Wang et al (2020) that consists of all publicly available observations of 6289 GRBs, from 1991 to 2016. They isolate those events with a measured redshift and observed duration of T90 > 2s (LGRBs), which can yield an estimate of the total isotropic gamma-ray emission, Eiso.…”

Section: Lloyd-ronning Aykutalp and Johnson (2019) (L19)mentioning

confidence: 99%

How unbiased statistical methods lead to biased scientific discoveries: A case study of the Efron-Petrosian statistic applied to the luminosity-redshift evolution of Gamma-Ray Bursts

Bryant,

Osborne,

Shahmoradi

2020

Preprint

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Statistical methods are frequently built upon assumptions that limit their applicability to certain problems and conditions. Failure to recognize these limitations can lead to conclusions that may be inaccurate or biased. An example of such methods is the non-parametric Efron-Petrosian test statistic used in the studies of truncated data. We argue and show how the inappropriate use of this statistical method can lead to biased conclusions when the assumptions under which the method is valid do not hold. We do so by reinvestigating the evidence recently provided by multiple independent reports on the evolution of the luminosity/energetics distribution of cosmological Long-duration Gamma-Ray Bursts (LGRBs) with redshift. We show that the effects of detection threshold has been likely significantly underestimated in the majority of previous studies. This underestimation of detection threshold leads to severely-incomplete LGRB samples that exhibit strong apparent luminosity-redshift or energetics-redshift correlations. We further confirm our findings by performing extensive Monte Carlo simulations of the cosmic rates and the luminosity/energy distributions of LGRBs and their detection process.

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Section: Lloyd-ronning Aykutalp and Johnson (2019) (L19)mentioning

confidence: 99%

How unbiased statistical methods lead to biased scientific discoveries: A case study of the Efron-Petrosian statistic applied to the luminosity-redshift evolution of Gamma-Ray Bursts

Bryant,

Osborne,

Shahmoradi

2020

Preprint

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“…The Amati relation is known to be not applicable for any GRBs with measured parameter pairs E iso , E p,i (see Qin & Chen 2013;Lin et al 2015); moreover, the regression parameters a, b slightly differ when obtained by different authors. Although the Amati relation was nevertheless concluded to be the preferable to other empirical relations for GRBs while constructing the Hubble diagram (Wang et al 2020), more detailed work is indeed needed in the future in the comparative analysis of various empirical relations and their possibly combined applications.…”

Section: Hubble Diagrammentioning

confidence: 99%

The Djorgovski–Gurzadyan dark energy integral equation and the Hubble diagram

Khachatryan

Степанян

2020

A&A

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We consider the observational aspects of the value of dark energy density from quantum vacuum fluctuations based initially on the Gurzadyan–Xue model. We reduce the Djorgovski–Gurzadyan integral equation to a differential equation for the co-moving horizon and then, by means of the obtained explicit form for the luminosity distance, we construct the Hubble diagram for two classes of observational samples. For supernova and gamma-ray burst data we show that this approach provides viable predictions for distances up to z ≃ 9, quantitatively at least as good as those provided by the Λ cold dark matter model. The Hubble parameter dependence H(z) of the two models also reveals mutual crossing at z = 0.4018, the interpretation of which is less evident.

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“…This translates to ∼ 0.01M of heavy r-process material for a europium mass fraction of X Eu = 10 −3 . The istropic γ-ray energy of GW170817 was E γ,iso,GW 170817 = 2.1 +6.4 −1.5 × 10 52 ergs (Hajela et al 2019), and from a sample of 468 LGRBs, the mean istropic energy of LGRBs is E γ,iso,LGRBs ≈ 2.6 +2.7 −0.5 × 10 53 ergs (Wang et al 2020). With these values: M r,collapsar ∼ M r,GW 170817 E γ,iso,LGRBs E γ,iso,GW 170817 ∼ 0.1M (see also Siegel 2020).…”

Section: Implications Of M R : the Amount Of R-processmentioning

confidence: 99%

Collapsar R-Process Yields Can Reproduce [Eu/Fe] Abundance Scatter in Metal-Poor Stars

Brauer,

Ji,

Drout

et al. 2020

Preprint

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It is unclear if neutron star mergers can explain the observed r-process abundances of metal-poor stars. Collapsars, defined here as rotating massive stars whose collapse results in a rapidly accreting disk around a black hole that can launch jets, are a promising alternative. We find that we can produce a self-consistent model in which a population of collapsars with stochastic europium yields synthesizes all of the r-process material in metal-poor ([Fe/H] < −2.5) stars. Our model reproduces the observed scatter and evolution of scatter of [Eu/Fe] abundances. We find that if collapsars are the dominant r-process site for metal-poor stars, r-process synthesis may be linked to supernovae that produce long γ-ray bursts. Our results also allow for the possibility that core-collapse supernovae beyond those that launch γ-ray bursts also produce r-process material (e.g., potentially a subset of Type Ic-BL supernovae). Furthermore, we identify collapsar jet properties (isotropic energy, engine luminosity, or engine time) which may trace r-process yield and verify that the amount of r-process yield produced per collapsar in our model (∼ 0.07M ) is consistent with other independent estimates. In the future, achieving 0.05 dex precision on distribution scatter or a reliable selection function would further constrain our probe of r-process production. Our model would also hold for another prompt r-process site with a power-law yield, and work is needed to determine if, for example, fast-merging neutron stars can also explain abundance scatter.

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A Comprehensive Statistical Study of Gamma-Ray Bursts

Cited by 37 publications

References 562 publications

How unbiased statistical methods lead to biased scientific discoveries: A case study of the Efron-Petrosian statistic applied to the luminosity-redshift evolution of Gamma-Ray Bursts

How unbiased statistical methods lead to biased scientific discoveries: A case study of the Efron-Petrosian statistic applied to the luminosity-redshift evolution of Gamma-Ray Bursts

The Djorgovski–Gurzadyan dark energy integral equation and the Hubble diagram

Collapsar R-Process Yields Can Reproduce [Eu/Fe] Abundance Scatter in Metal-Poor Stars

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