The Stellar-mass Function of Long Gamma-Ray Burst Host Galaxies

Guang-Xuan, Lan,; Wei, Jun-Jie; Li, Ye; Zeng, Houdun; Wu, Xue-Feng

doi:10.3847/1538-4357/ac8fec

Cited by 6 publications

(9 citation statements)

References 78 publications

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“…All best-fitting parameters are collected in Overall, based on the results of different subsamples, it is more likely that the energy/luminosity function of GRBs always follows the CPL or BPL model, namely, there is always a cutoff or break at the high-energy end. This conclusion is consistent with results from previous studies both on luminosity function (Liang et al 2007;Virgili et al 2009;Wanderman & Piran 2010;Sun et al 2015) and energy function (Atteia et al 2017;Lan et al 2022).…”

Section: Energy Distribution Functionsupporting

confidence: 93%

GRB 221009A: An Ordinary Nearby GRB with Extraordinary Observational Properties

Lin

Liu

Xiao

et al. 2023

ApJL

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The gamma-ray burst GRB 221009A, known as the “brightest of all time,” is the closest energetic burst detected so far, with an energy of E γ,iso ∼ 1055 erg. This study aims to assess its compatibility with known GRB energy and luminosity distributions. Our analysis indicates that the energy/luminosity function of GRBs is consistent across various redshift intervals, and that the inclusion of GRB 221009A does not significantly impact the function at low redshifts. Additionally, our evaluation of the best-fitting result of the entire GRB sample suggests that the expected number of GRBs with energy greater than 1055 erg at a low redshift is 0.2, so that the emergence of GRB 221009A is consistent with expected energy/luminosity functions within ∼2σ Poisson fluctuation error, still adhering to the principles of small number statistics. Furthermore, we find that GRB 221009A and other energetic bursts, defined as E γ,iso ≳ 1054 erg, exhibit no significant differences in terms of distributions of T 90, minimum timescale, Amati relation, E γ,iso–E X,iso relation, L γ,iso–Γ0 relation, E γ,iso–Γ0 relation, L γ,iso–E p,i–Γ0 relation, and host galaxy properties, compared to normal long GRBs. This suggests that energetic GRBs (including GRB 221009A) and other long GRBs likely have similar progenitor systems and undergo similar energy dissipation and radiation processes. The generation of energetic GRBs may be due to more extreme central engine properties or, more likely, a rarer viewing configuration of a quasi-universal structured jet.

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Section: Energy Distribution Functionsupporting

confidence: 93%

GRB 221009A: An Ordinary Nearby GRB with Extraordinary Observational Properties

Lin

Liu

Xiao

et al. 2023

ApJL

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show abstract

“…Overall, based on the results of different subsamples, it is more likely that the energy/luminosity function of GRBs always follows the CPL or BPL model, namely, there is always a cutoff or break at the high energy end. This conclusion is consistent with results from previous studies both on luminosity function (Liang et al 2007;Virgili et al 2009;Wanderman & Piran 2010;Sun et al 2015) and energy function (Atteia et al 2017;Lan et al 2022).…”

Section: Energy Distribution Functionsupporting

confidence: 93%

GRB 221009A: An ordinary nearby GRB with extraordinary observational properties

Liu¹,

He²,

Liu³

et al. 2023

Preprint

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The gamma-ray burst GRB 221009A, known as the "brightest-of-all-time" (BOAT), is the closest energetic burst detected so far, with an energy of E γ,iso ∼ 10 55 ergs. This study aims to assess its compatibility with known GRB energy and luminosity distributions. Our analysis indicates that the energy/luminosity function of GRBs is consistent across various redshift intervals, and that the inclusion of GRB 221009A does not significantly impact the function at low redshifts. Additionally, our evaluation of the best-fitting result of the entire GRB sample suggests that the expected number of GRBs with energy greater than 10 55 ergs at a low redshift is 0.2, so that the emergence of GRB 221009A is consistent with expected energy/luminosity functions within ∼ 2σ Poisson fluctuation error, still adhering to the principles of small number statistics. Furthermore, we find that GRB 221009A and other energetic bursts, defined as E γ,iso 10 54 ergs, exhibit no significant differences in terms of distributions of T 90 , minimum timescale, Amati relation, E γ,iso -E X,iso relation, L γ,iso − Γ 0 relation, E γ,iso − Γ 0 relation, L γ,iso − E p,i − Γ 0 relation, and host galaxy properties, compared to normal long GRBs. This suggests that energetic GRBs (including GRB 221009A) and other long GRBs likely have similar progenitor systems and undergo similar energy dissipation and radiation processes. The generation of energetic GRBs may be due to more extreme central engine properties or, more likely, a rarer viewing configuration of a quasi-universal structured jet.

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“…The short lifetimes of Type II GRB progenitors (Woosley et al 2002) make their event rate generally follow the star formation rate (SFR) of the host galaxies, and Type II GRB host galaxies generally have higher SFR. (Bloom et al 2002;Chary et al 2007;Savaglio et al 2009;Levesque et al 2010a;Levesque 2014;Robertson & Ellis 2011;Wei et al 2014;Cucchiara et al 2015;Trenti et al 2015;Lan et al 2022). The redshift distribution of Type I GRBs are found to be delayed with respect to the star formation history, and thus host galaxies of Type I GRBs generally have lower SFR respectively (Piran 1992;Nakar et al 2006;Zheng & Ramirez-Ruiz 2007;Virgili et al 2011;Wanderman & Piran 2015;Luo et al 2022).…”

Section: Host Galaxymentioning

confidence: 94%

Identifying the Physical Origin of Gamma-Ray Bursts with Supervised Machine Learning

Luo,

Wang,

Zhu-Ge

et al. 2023

ApJ

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The empirical classification of gamma-ray bursts (GRBs) into long and short GRBs based on their durations is already firmly established. This empirical classification is generally linked to the physical classification of GRBs originating from compact binary mergers and GRBs originating from massive star collapses, or Type I and II GRBs, with the majority of short GRBs belonging to Type I and the majority of long GRBs belonging to Type II. However, there is a significant overlap in the duration distributions of long and short GRBs. Furthermore, some intermingled GRBs, i.e., short-duration Type II and long-duration Type I GRBs, have been reported. A multiparameter classification scheme of GRBs is evidently needed. In this paper, we seek to build such a classification scheme with supervised machine-learning methods, chiefly XGBoost. We utilize the GRB Big Table and Greiner’s GRB catalog and divide the input features into three subgroups: prompt emission, afterglow, and host galaxy. We find that the prompt emission subgroup performs the best in distinguishing between Type I and II GRBs. We also find the most important distinguishing features in prompt emission to be T 90, the hardness ratio, and fluence. After building the machine-learning model, we apply it to the currently unclassified GRBs to predict their probabilities of being either GRB class, and we assign the most probable class of each GRB to be its possible physical class.

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The Stellar-mass Function of Long Gamma-Ray Burst Host Galaxies

Cited by 6 publications

References 78 publications

GRB 221009A: An Ordinary Nearby GRB with Extraordinary Observational Properties

GRB 221009A: An Ordinary Nearby GRB with Extraordinary Observational Properties

GRB 221009A: An ordinary nearby GRB with extraordinary observational properties

Identifying the Physical Origin of Gamma-Ray Bursts with Supervised Machine Learning

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