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.
Desmond, (Exhaustive) Symbolic Regression and model selection by minimum description length (2025), arXiv:2507.13033 [astro-ph.IM]
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Bootstrap-based symbolic regression on supernova and BAO data finds mild 2-4 sigma deviations from FLRW consistency relations, which if real would rule out most FLRW-based solutions to cosmological tensions.
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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.
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Model-independent constraints on generalized FLRW consistency relations with bootstrap-based symbolic regression
Bootstrap-based symbolic regression on supernova and BAO data finds mild 2-4 sigma deviations from FLRW consistency relations, which if real would rule out most FLRW-based solutions to cosmological tensions.