Inverse Cosmography: testing the effectiveness of cosmographic polynomials using machine learning

DOI10.1088/1475-7516/2020/12/007zbMath1484.85019arXiv2005.02807OpenAlexW3020836893MaRDI QIDQ5027069

Celia Escamilla-Rivera, Cristian Muñoz

Publication date: 3 February 2022

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

Full work available at URL: https://arxiv.org/abs/2005.02807

zbMATH Keywords

modified gravitydark energy theorysupernova type Ia-standard candles

Mathematics Subject Classification ID

Inverse scattering problems in quantum theory (81U40) Relativistic gravitational theories other than Einstein's, including asymmetric field theories (83D05) Galactic and stellar structure (85A15) Dark matter and dark energy (83C56)

Related Items (2)

Dynamical dark energy models in the light of gravitational-wave transient catalogues ⋮ Neural networks and standard cosmography with newly calibrated high redshift GRB observations

Cites Work

• The dynamics of quintessence, the quintessence of dynamics
• Extended gravity cosmography
• Connecting early and late epochs by f(z)CDM cosmography
• A deep learning approach to cosmological dark energy models

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