Machine learning cosmic backreaction and its effects on observations

Abstract

Symbolic expressions for cosmic backreaction and mean redshift drift in a range of 2-region models in terms of average quantities are presented. The demonstration that these expressions can be obtained constitutes the opening of a new avenue towards understanding the effects of cosmic backreaction in our universe: With a symbolic expression for the redshift drift at hand, the redshift drift can be used to constrain cosmological parameters including the large-scale expansion rate and backreaction. In addition, by introducing symbolic expressions for cosmic backreaction, this quantity can be constrained with observations such as redshift-distance measures.

arXiv comment6 pages, 3 captioned figures. Accepted for publication in PRL. v2: Updated reference list. No other changes

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• A precise symbolic emulator of the linear matter power spectrum
• Cosmic backreaction and the mean redshift drift from symbolic regression
• Future deceleration due to backreaction in a Universe with multiple inhomogeneous domains
• cp3-bench: A tool for benchmarking symbolic regression algorithms tested with cosmology
• Hubble Diagrams in Statistically Homogeneous, Anisotropic Universes
• Cosmological parameter constraints using phenomenological symbolic expressions: On the significance of symbolic expression complexity and accuracy
• Machine Learning-Based Analytical Expressions for Gray-Body Factors and Application to Primordial Black Holes
• Analyzing the 21-cm signal brightness temperature in the Universe with inhomogeneities
• Constraining the Hubble parameter with the 21 cm brightness temperature signal in a universe with inhomogeneities
• Conditional variational autoencoders for cosmological model discrimination and anomaly detection in cosmic microwave background power spectra