A deep learning approach to cosmological dark energy models

DOI10.1088/1475-7516/2020/03/008zbMath1490.83044arXiv1910.02788OpenAlexW2978226903MaRDI QIDQ5068403

Celia Escamilla-Rivera, Maryi A. Carvajal Quintero, Salvatore Capozziello

Publication date: 6 April 2022

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

Full work available at URL: https://arxiv.org/abs/1910.02788

zbMATH Keywords

modified gravitydark energy theorysupernova type Ia-standard candles

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Diffraction, scattering (78A45) Neural nets applied to problems in time-dependent statistical mechanics (82C32) Electromagnetic fields in general relativity and gravitational theory (83C50) Relativistic gravitational theories other than Einstein's, including asymmetric field theories (83D05) Galactic and stellar structure (85A15) Radiative transfer in astronomy and astrophysics (85A25) Dark matter and dark energy (83C56)

Related Items (9)

Barrow holographic dark energy with hybrid expansion law ⋮ Deep learning of CMB radiation temperature ⋮ Connecting early and late epochs by f(z)CDM cosmography ⋮ Building healthy Lagrangian theories with machine learning ⋮ Neural networks and standard cosmography with newly calibrated high redshift GRB observations ⋮ Barrow holographic dark energy with hubble horizon as IR cutoff ⋮ Generalized Barrow entropic holographic dark energy with Granda–Oliver cut-off ⋮ Inverse Cosmography: testing the effectiveness of cosmographic polynomials using machine learning ⋮ Neural network reconstruction of H'(z) and its application in teleparallel gravity

Uses Software

• Scikit
• TensorFlow
• SuperNNova
• CosmoFlow

Cites Work

• Unnamed Item
• The dynamics of quintessence, the quintessence of dynamics
• Dark Energy: Investigation and Modeling
• The cosmological constant and dark energy
• LII. An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, F. R. S. communicated by Mr. Price, in a letter to John Canton, A. M. F. R. S
• Bayesian Logical Data Analysis for the Physical Sciences
• CURVATURE QUINTESSENCE
• Extended gravity cosmography
• Measurements of Ω and Λ from 42 High‐Redshift Supernovae
• Nested sampling for general Bayesian computation

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