Bayesian reconstruction and exhaustive symbolic regression on CMB, BAO, and supernova data yield the one-parameter dark energy parametrization w(a) = w0 / sqrt(a) that fits observations comparably to CPL and better than LambdaCDM.
Desmond, (Exhaustive) Symbolic Regression and model selection by minimum description length (2025), arXiv:2507.13033 [astro-ph.IM]
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 3roles
background 1polarities
background 1representative citing papers
Exhaustive symbolic regression identifies low-complexity functional forms for luminosity and mass functions that outperform Schechter and Press-Schechter parametrizations while satisfying physical extrapolation and integration constraints.
Bootstrap symbolic regression of SN+BAO data yields mild 2–4σ FLRW consistency deviations and a density field still compatible with both Planck and SH0ES ΛCDM.
citing papers explorer
-
Data-Driven Discovery of a Simple Phantom-Crossing Dark Energy Parametrization
Bayesian reconstruction and exhaustive symbolic regression on CMB, BAO, and supernova data yield the one-parameter dark energy parametrization w(a) = w0 / sqrt(a) that fits observations comparably to CPL and better than LambdaCDM.
-
The functional form of galaxy and halo luminosity and mass functions
Exhaustive symbolic regression identifies low-complexity functional forms for luminosity and mass functions that outperform Schechter and Press-Schechter parametrizations while satisfying physical extrapolation and integration constraints.
-
Model-independent constraints on generalized FLRW consistency relations with bootstrap-based symbolic regression
Bootstrap symbolic regression of SN+BAO data yields mild 2–4σ FLRW consistency deviations and a density field still compatible with both Planck and SH0ES ΛCDM.