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arXiv · 2604.11883

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

Abstract

Recent analyses combining DESI DR2 BAO with CMB and SNe Ia data have reported $2.8$--$4.2\sigma$ 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 $X(z) \equiv \rho_{\rm DE}(z)/\rho_{\rm DE}(0)$. Flux averaging reduces the $\Omega_m$ difference between SNe and DESI from ${\sim}2\sigma$ to ${\sim}1\sigma$ for Pantheon+ and DES-Dovekie. The reconstructed $X(z)$ for DESI DR2 + CMB + SNe is consistent with $\Lambda$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 ${\sim}2.7\sigma$ for Pantheon+ but only $1.6$--$1.7\sigma$ for Pantheon and DES-Dovekie. The $X(z)$ for DESI DR2 + CMB + Union3 is consistent with these within $1\sigma$, but shows an additional $2.4\sigma$ deviation at $z=1/3$ besides the ${\sim}2.7\sigma$ deviation at $z=2/3$. Across all analyses, the departure from $\Lambda$CDM correlates with each dataset's $\Omega_m$ preference. We demonstrate that a pure $\Lambda$CDM universe with the measured $\Omega_m$ 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 $\Omega_m$ constraints will be essential to determine whether the signal reflects genuine dark energy evolution or residual inter-probe $\Omega_m$ inconsistencies.

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Zhenyuan Wang, Yun Wang. 2026-04-13. Model-Independent Analysis of Type Ia Supernova Datasets and Implications for Dark Energy. https://arxiv.org/abs/2604.11883

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