Neutrino Mass Priors for Cosmology from Random Matrices
Andrew J. Long, Marco Raveri, Wayne Hu, and Scott Dodelson

TL;DR
This paper proposes physically motivated priors for the sum of neutrino masses based on random matrix theory, influencing cosmological constraints and potentially revealing the neutrino mass generation mechanism.
Contribution
It introduces a novel approach to set neutrino mass priors using random matrix models, linking particle physics theories to cosmological data analysis.
Findings
Prior peaks near the minimal allowed neutrino mass due to eigenvalue repulsion.
Different mass generation models produce distinguishable priors.
Provides fitting functions for practical application of these priors.
Abstract
Cosmological measurements of structure are placing increasingly strong constraints on the sum of the neutrino masses, , through Bayesian inference. Because these constraints depend on the choice for the prior probability , we argue that this prior should be motivated by fundamental physical principles rather than the ad hoc choices that are common in the literature. The first step in this direction is to specify the prior directly at the level of the neutrino mass matrix , since this is the parameter appearing in the Lagrangian of the particle physics theory. Thus by specifying a probability distribution over , and by including the known squared mass splittings, we predict a theoretical probability distribution over that we interpret as a Bayesian prior probability . We find that peaks…
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Taxonomy
TopicsNeutrino Physics Research · Particle physics theoretical and experimental studies · Astrophysics and Cosmic Phenomena
