Elderly probe nails gamma-ray bursts

Nature ◽  
2005 ◽  
Author(s):  
Mark Peplow
Keyword(s):  
Gamma Ray ◽  
Gamma Ray Bursts

On the nature of cosmic gamma-ray bursts

1996 ◽  
Vol 166 (7) ◽  
pp. 743-762 ◽  
Author(s):  
B.I. Luchkov ◽  
I.G. Mitrofanov ◽  
I.L. Rozental'
Keyword(s):  
Gamma Ray ◽  
Gamma Ray Bursts

Cosmic gamma-ray bursts and soft gamma-repeaters — observations and modeling of extreme astrophysical phenomena (100th anniversary of the Ioffe Institute)

2018 ◽  
Vol 189 (08) ◽  
pp. 785-802 ◽  
Author(s):  
Rafail L. Aptekar ◽  
Andrei M. Bykov ◽  
Sergei V. Golenetskii ◽  
Dmitrii D. Frederiks ◽  
Dmitry S. Svinkin ◽  
...  
Keyword(s):  
Gamma Ray ◽  
100Th Anniversary ◽  
Gamma Ray Bursts ◽  
Soft Gamma Repeaters ◽  
Ioffe Institute ◽  
Astrophysical Phenomena

Constraints on Association of Single‐Pulse Gamma‐Ray Bursts and Supernovae

1999 ◽  
Vol 518 (2) ◽  
pp. 901-908 ◽  
Author(s):  
J. P. Norris ◽  
J. T. Bonnell ◽  
K. Watanabe
Keyword(s):  
Gamma Ray ◽  
Single Pulse ◽  
Gamma Ray Bursts

Can Precessing Jets Explain the Light Curves of Gamma‐Ray Bursts?

1999 ◽  
Vol 520 (2) ◽  
pp. 666-679 ◽  
Author(s):  
Simon F. Portegies Zwart ◽  
Chang‐Hwan Lee ◽  
Hyun Kyu Lee
Keyword(s):  
Gamma Ray ◽  
Gamma Ray Bursts ◽  
Light Curves

The X-ray luminosity function of short gamma-ray bursts

2021 ◽  
Vol 366 (4) ◽  
Author(s):  
Zhi-Ying Liu ◽  
Fu-Wen Zhang ◽  
Si-Yuan Zhu
Keyword(s):  
Luminosity Function ◽  
Gamma Ray ◽  
Gamma Ray Bursts ◽  
X Ray

scholarly journals Model-independent calibrations of gamma-ray bursts using machine learning

2021 ◽  
Vol 503 (3) ◽  
pp. 4581-4600
Author(s):  
Orlando Luongo ◽  
Marco Muccino
Keyword(s):  
Machine Learning ◽  
Dark Energy ◽  
Gamma Ray ◽  
Gamma Ray Bursts ◽  
Bayesian Regression ◽  
Machine Learning Techniques ◽  
Acoustic Oscillations ◽  
Chi Square ◽  
Model Independent ◽  
Chi Square Analysis

ABSTRACT We alleviate the circularity problem, whereby gamma-ray bursts are not perfect distance indicators, by means of a new model-independent technique based on Bézier polynomials. We use the well consolidate Amati and Combo correlations. We consider improved calibrated catalogues of mock data from differential Hubble rate points. To get our mock data, we use those machine learning scenarios that well adapt to gamma-ray bursts, discussing in detail how we handle small amounts of data from our machine learning techniques. We explore only three machine learning treatments, i.e. linear regression, neural network, and random forest, emphasizing quantitative statistical motivations behind these choices. Our calibration strategy consists in taking Hubble’s data, creating the mock compilation using machine learning and calibrating the aforementioned correlations through Bézier polynomials with a standard chi-square analysis first and then by means of a hierarchical Bayesian regression procedure. The corresponding catalogues, built up from the two correlations, have been used to constrain dark energy scenarios. We thus employ Markov chain Monte Carlo numerical analyses based on the most recent Pantheon supernova data, baryonic acoustic oscillations, and our gamma-ray burst data. We test the standard ΛCDM model and the Chevallier–Polarski–Linder parametrization. We discuss the recent H0 tension in view of our results. Moreover, we highlight a further severe tension over Ωm and we conclude that a slight evolving dark energy model is possible.

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