Constraining neutrino mass in dynamical dark energy cosmologies with the logarithm parametrization and the oscillating parametrization

TL;DR

This study constrains neutrino mass within dynamical dark energy models using logarithmic and oscillating parametrizations, showing these models fit observational data well and can alter neutrino mass constraints compared to standard models.

Contribution

It introduces and tests two novel dynamical dark energy parametrizations and compares their impact on neutrino mass constraints with traditional models.

Findings

Both models fit observational data comparably to CPL.

Dark energy models significantly affect neutrino mass constraints.

Degenerate neutrino hierarchy yields the tightest mass bounds.

Abstract

We constrain two dynamical dark energy models that are parametrized by the logarithm form of $w(z)=w_{0}+w_{1}\left(\frac{\ln (2+z)}{1+z}-\ln 2\right)$ and the oscillating form of $w(z)=w_{0}+w_{1}\left(\frac{\sin(1+z)}{1+z}-\sin(1)\right)$. Comparing with the Chevallier-Polarski-Linder (CPL) model, the two parametrizations for dark energy can explore the whole evolution history of the universe properly. Using the current mainstream observational data including the cosmic microwave background data and the baryon acoustic oscillation data as well as the type Ia supernovae data, we perform the $\chi^2$ statistic analysis to global fit these models, finding that the logarithm parametrization and the oscillating parametrization are almost as well as the CPL scenario in fitting these data. We make a comparison for the impacts of the dynamical dark energy on the cosmological constraints on the total mass of active neutrinos. We find that the dark energy properties could significantly change the fitting results of neutrino mass. Looser constraints on $\sum m_{\nu}$ are obtained in the logarithm and oscillating models than those derived in the CPL model. Consideration of the possible mass ordering of neutrinos reveals that the most stringent constraint on $\sum m_{\nu}$ appears in the degenerate hierarchy case.