Neural networks and standard cosmography with newly calibrated high redshift GRB observations

DOI10.1088/1475-7516/2022/04/016zbMath1506.83039arXiv2109.00636OpenAlexW3196810790MaRDI QIDQ5099248

Celia Escamilla-Rivera, Maryi Carvajal, Cristian Zamora, Martin A. Hendry

Publication date: 31 August 2022

Published in: Journal of Cosmology and Astroparticle Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2109.00636

zbMATH Keywords

modified gravitysupernova type Ia-standard candlesgamma ray bursts theory

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Relativistic cosmology (83F05) Diffraction, scattering (78A45) Geometrodynamics and the holographic principle (83E05) Relativistic gravitational theories other than Einstein's, including asymmetric field theories (83D05) Galactic and stellar structure (85A15) Radiative transfer in astronomy and astrophysics (85A25) Neural nets and related approaches to inference from stochastic processes (62M45) Dark matter and dark energy (83C56)

Cites Work

• Extended gravity cosmography
• Inverse Cosmography: testing the effectiveness of cosmographic polynomials using machine learning
• Constraining extra dimensions on cosmological scales with LISA future gravitational wave siren data
• Probing homogeneity with standard candles
• A deep learning approach to cosmological dark energy models
• Cosmological viable models in f ( T , B ) theory as solutions to the H ₀ tension

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