Model-independent calibrations of gamma-ray bursts using machine learning
Luongo O. Muccino M.
1 May 2021Oxford University Press
Monthly Notices of the Royal Astronomical Society
2021#503Issue 34581 - 4600 pp.
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 Hubbles 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 ∆CDM 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.
Cosmology: dark energy , Cosmology: observations , Gamma-ray burst: general
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Scuola di Scienze e Tecnologie, Università di Camerino, Via Madonna delle Carceri 9, Camerino, I-62032, Italy
Dipartimento di Matematica, Università di Pisa, Largo B. Pontecorvo 5, Pisa, I-56127, Italy
NNLOT, Al-Farabi Kazakh National University, Al-Farabi av. 71, Almaty, 050040, Kazakhstan
Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali di Frascati, Via Enrico Fermi 54, Frascati, I-00044, Italy
Scuola di Scienze e Tecnologie
Dipartimento di Matematica
NNLOT
Istituto Nazionale di Fisica Nucleare
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