Reconstruction of dark energy and late-time cosmic expansion using the Weighted Function Regression method
Alex Gonz\'alez-Fuentes, Adri\`a G\'omez-Valent

TL;DR
This paper employs the Weighted Function Regression method to reconstruct dark energy evolution, confirming its dynamical nature and transition from phantom to quintessence, while addressing biases from traditional parametrizations.
Contribution
It introduces the WFR method to reduce subjectivity in dark energy modeling and provides model-agnostic reconstructions confirming dynamical dark energy.
Findings
Evidence favors a transition from phantom to quintessence dark energy.
The probability of phantom crossing is between 96.21% and 99.97%.
No significant evidence for negative dark energy density below redshift 2.5-3.
Abstract
Recent data from multiple supernova catalogs and DESI, when combined with CMB, suggest a non-trivial evolution of dark energy (DE) at the CL. This evidence is typically quantified using the CPL parametrization of the DE equation-of-state parameter which corresponds to a first-order Taylor expansion around . However, this truncation is to some extent arbitrary and may bias our interpretation of the data, potentially leading us to mistake spurious features of the best-fit CPL model for genuine physical properties of DE. In this work, we apply the Weighted Function Regression (WFR) method to eliminate the subjectivity associated with the choice of truncation order. We assign Bayesian weights to the various orders and compute weighted posterior distributions of the quantities of interest. Using this model-agnostic approach, we reconstruct some of the most relevant…
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