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Strong Bayesian Evidence for the Normal Neutrino Hierarchy

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arxiv 1703.03425 v2 pith:3BX3B3P2 submitted 2017-03-09 astro-ph.CO hep-ph

Strong Bayesian Evidence for the Normal Neutrino Hierarchy

classification astro-ph.CO hep-ph
keywords massesneutrinonormaldatahierarchytextbayesiancosmological
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The configuration of the three neutrino masses can take two forms, known as the normal and inverted hierarchies. We compute the Bayesian evidence associated with these two hierarchies. Previous studies found a mild preference for the normal hierarchy, and this was driven by the asymmetric manner in which cosmological data has confined the available parameter space. Here we identify the presence of a second asymmetry, which is imposed by data from neutrino oscillations. By combining constraints on the squared-mass splittings with the limit on the sum of neutrino masses of $\Sigma m_\nu < 0.13$ eV, and using a minimally informative prior on the masses, we infer odds of 42:1 in favour of the normal hierarchy, which is classified as "strong" in the Jeffreys' scale. We explore how these odds may evolve in light of higher precision cosmological data, and discuss the implications of this finding with regards to the nature of neutrinos. Finally the individual masses are inferred to be $m_1 = 3.80^{+26.2}_{-3.73} \, \text{meV}, m_2 = 8.8^{+18}_{-1.2} \, \text{meV}, m_3 = 50.4^{+5.8}_{-1.2} \, \text{meV}$ ($95\%$ credible intervals).

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. From Evidence to Evident: Decisive Cosmological Evidence for the Normal Neutrino Mass Hierarchy

    astro-ph.CO 2026-06 unverdicted novelty 6.0

    DESI DR2 data constrain the neutrino mass sum below the inverted hierarchy minimum, yielding Bayes factor K>460 for normal hierarchy in standard cosmology.

  2. The Coherence Principle: A Falsifiable Prior for Model Selection from the Grammar of Theories

    astro-ph.CO 2026-06 unverdicted novelty 6.0

    The Coherence Principle supplies a falsifiable prior for Bayesian model selection by converting compatibility with a theory's validated grammar into prior weights via a maximum-entropy exponential controlled by one parameter.