# Cosmology-marginalized approaches in Bayesian model comparison: the neutrino mass as a case study

## TL;DR

This paper introduces a new Bayesian method to derive robust cosmological parameter constraints that account for model dependence, with a case study on neutrino mass bounds relevant to dark matter and future neutrino experiments.

## Contribution

It presents a novel approach to marginalize over cosmological models in Bayesian analysis, improving the robustness of parameter constraints compared to traditional methods.

## Key findings

- Preferred cosmological models are less favored when considering multiple models.
- The method provides model-independent bounds on neutrino mass.
- Application to neutrino mass yields more reliable limits for dark matter and experimental planning.

## Abstract

We propose here a \emph{novel} method which singles out the \emph{a priori} unavoidable dependence on the underlying cosmological model when extracting parameter constraints, providing robust limits which only depend on the considered dataset. Interestingly, when dealing with several possible cosmologies and interpreting the Bayesian preference in terms of the Gaussian statistical evidence, the preferred model is much less favored than when only two cases are compared. As a working example, we apply our approach to the cosmological neutrino mass bounds, which play a fundamental role not only in establishing the contribution of relic neutrinos to the dark matter of the Universe, but also in the planning of future experimental searches of the neutrino character and of the neutrino mass ordering.