Constraints on cubic and \(f(P)\) gravity from the cosmic chronometers, BAO \& CMB datasets: use of machine learning algorithms
DOI10.1016/j.nuclphysb.2022.115746zbMath1495.83071arXiv2107.12417OpenAlexW4226407040MaRDI QIDQ2131839
Publication date: 27 April 2022
Published in: Nuclear Physics. B (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2107.12417
Measures of association (correlation, canonical correlation, etc.) (62H20) Monte Carlo methods (65C05) Learning and adaptive systems in artificial intelligence (68T05) Relativistic cosmology (83F05) Diffraction, scattering (78A45) Einstein's equations (general structure, canonical formalism, Cauchy problems) (83C05) Neural nets applied to problems in time-dependent statistical mechanics (82C32) Gravitational energy and conservation laws; groups of motions (83C40) Relativistic gravitational theories other than Einstein's, including asymmetric field theories (83D05) Constrained dynamics, Dirac's theory of constraints (70H45) Peak functions (32T40)
Uses Software
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