Model-Independent Analysis of Type Ia Supernova Datasets and Implications for Dark Energy

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Model-Independent Analysis of Type Ia Supernova Datasets and Implications for Dark Energy

Authors

Zhenyuan Wang, Yun Wang

Abstract

Recent analyses combining DESI DR2 BAO with CMB and SNe Ia data have reported $2.8$--$4.2σ$ evidence for dynamical dark energy, but the significance depends strongly on the supernova sample, raising the question of whether this signal reflects new physics, dataset-specific systematics, or the choice of dark energy parameterization. We investigate this question by analyzing four SNe Ia compilations (Pantheon, Pantheon+, DES-Dovekie, and Union3) with DESI DR2 BAO and Planck CMB distance priors, using flux averaging, model-independent expansion rate extraction, parametric ($w_0 w_a$CDM) fits, and a non-parametric reconstruction of the dark energy density ratio $X(z) \equiv ρ_{\rm DE}(z)/ρ_{\rm DE}(0)$. Flux averaging reduces the $Ω_m$ difference between SNe and DESI from ${\sim}2σ$ to ${\sim}1σ$ for Pantheon+ and DES-Dovekie. The reconstructed $X(z)$ for DESI DR2 + CMB + SNe is consistent with $Λ$CDM for Pantheon, Pantheon+, and DES-Dovekie except at $0.5<z<1$, consistent with Wang \& Freese (2026). The largest deviation occurs at $z=2/3$, reaching ${\sim}2.7σ$ for Pantheon+ but only $1.6$--$1.7σ$ for Pantheon and DES-Dovekie. The $X(z)$ for DESI DR2 + CMB + Union3 is consistent with these within $1σ$, but shows an additional $2.4σ$ deviation at $z=1/3$ besides the ${\sim}2.7σ$ deviation at $z=2/3$. Across all analyses, the departure from $Λ$CDM correlates with each dataset's $Ω_m$ preference. We demonstrate that a pure $Λ$CDM universe with the measured $Ω_m$ differences can reproduce the observed $X(z)$ pattern, providing a viable alternative interpretation of the observed $X(z) \neq 1$ pattern. Future surveys by Euclid and Roman with sub-percent $Ω_m$ constraints will be essential to determine whether the signal reflects genuine dark energy evolution or residual inter-probe $Ω_m$ inconsistencies.

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