The Impact of $\Omega_{m0}$ Prior Bias on Cosmological Parameter Estimation: Reconciling DESI DR2 BAO and Pantheon+ SNe Data Combination Results
Seokcheon Lee

TL;DR
This paper investigates how prior biases in matter density influence cosmological parameter estimates, showing that tensions between datasets can cause systematic biases that mimic deviations from the standard model.
Contribution
It demonstrates through mock data analysis that prior biases in $ m extit{O}_m$ can lead to significant parameter estimation biases, explaining current tensions without new physics.
Findings
Biases in $ m extit{O}_m$ priors cause shifts in dark energy parameters.
Mock data shows biases can mimic non-$ m extit{Lambda}$CDM results.
Tensions between datasets may be statistical, not physical.
Abstract
Recent cosmological parameter analyses combining DESI DR2 Baryon Acoustic Oscillation (BAO) data with external probes, such as Pantheon+ Supernovae (SNe) observations, have reported deviations of the dark energy equation-of-state parameters () from the standard CDM model predictions (). A notable aspect of these results is the role of prior information from SNe, which is known to exhibit tension with BAO-only constraints. In this study, we rigorously investigate this effect through a statistical analysis using 1000 mock DESI DR2 BAO data realizations. We demonstrate that the strong degeneracy between , , and causes significant biases in the estimated dark energy parameters when the prior mean deviates from its true underlying value. Specifically, applying an prior mean of 0.33 (consistent with some SNe-only…
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