A novel approach to quantifying the sensitivity of current and future cosmological datasets to the neutrino mass ordering through Bayesian hierarchical modeling
Martina Gerbino, Massimiliano Lattanzi, Olga Mena, Katherine Freese

TL;DR
This paper introduces a Bayesian hierarchical modeling approach to constrain neutrino masses from cosmological data, accounting for the unknown mass hierarchy, and forecasts the potential to determine the hierarchy with future surveys.
Contribution
The paper presents a new Bayesian hierarchical method that marginalizes over neutrino mass ordering, providing unbiased constraints and hierarchy preference from cosmological datasets.
Findings
Current data cannot distinguish neutrino mass hierarchy.
Adding BAO data weakly favors normal hierarchy.
Future surveys could determine hierarchy if total mass is near minimal value.
Abstract
We present a novel approach to derive constraints on neutrino masses from cosmological data, while taking into account our ignorance of the neutrino mass ordering. We derive constraints from a combination of current and future cosmological datasets on the total neutrino mass and on the mass fractions carried by each of the mass eigenstates, after marginalizing over the (unknown) neutrino mass ordering, either normal (NH) or inverted (IH). The bounds take therefore into account the uncertainty related to our ignorance of the mass hierarchy. This novel approach is carried out in the framework of Bayesian analysis of a typical hierarchical problem. In this context, the choice of the neutrino mass ordering is modeled via the discrete hyperparameter . The preference for either the NH or the IH scenarios is then encoded in the posterior distribution of itself.…
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