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 -- 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 (CDM) fits, and a non-parametric reconstruction of the dark energy density ratio . Flux averaging reduces the difference between SNe and DESI from to for Pantheon+ and DES-Dovekie. The reconstructed for DESI DR2 + CMB + SNe is consistent with CDM for Pantheon, Pantheon+, and DES-Dovekie except at , consistent with Wang \& Freese (2026). The largest deviation occurs at , reaching for Pantheon+ but only -- for Pantheon and DES-Dovekie. The for DESI DR2 + CMB + Union3 is consistent with these within , but shows an additional deviation at besides the deviation at . Across all analyses, the departure from CDM correlates with each dataset's preference. We demonstrate that a pure CDM universe with the measured differences can reproduce the observed pattern, providing a viable alternative interpretation of the observed pattern. Future surveys by Euclid and Roman with sub-percent constraints will be essential to determine whether the signal reflects genuine dark energy evolution or residual inter-probe inconsistencies.
Cite
@article{arxiv.2604.11883,
title = {Model-Independent Analysis of Type Ia Supernova Datasets and Implications for Dark Energy},
author = {Zhenyuan Wang and Yun Wang},
journal= {arXiv preprint arXiv:2604.11883},
year = {2026}
}
Comments
To be submitted to JCAP. Comments are welcome. Flux-averaging code to be publicly released; currently available by request