Strong Bayesian Evidence for the Normal Neutrino Hierarchy - INSPIRE

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Strong Bayesian Evidence for the Normal Neutrino Hierarchy

Fergus Simpson
(
- ICC, Barcelona U.
)
,
Raul Jimenez
(
- ICC, Barcelona U. and
- ICREA, Barcelona
)
,
Carlos Pena-Garay
(
- LSC, Canfranc
)
,
Licia Verde
(
- ICC, Barcelona U. and
- ICREA, Barcelona
)

Mar 9, 2017

14 pages

Published in:

JCAP 06 (2017) 029

Published: Jun 14, 2017

e-Print:

1703.03425 [astro-ph.CO]

DOI:

10.1088/1475-7516/2017/06/029

PDG: SUM OF THE NEUTRINO MASSES,

{\mathit m}_{\mathrm {tot}}

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Abstract: (IOP)

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 [1] with the limit on the sum of neutrino masses of Σm(ν) < 0.13 eV [2], 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)meV, m(2)=8.8(+18)(-1.2)meV, m(3)=50.4(+5.8)(-1.2)meV (95% credible intervals).

Note:

Matches accepted version in JCAP. We provide publicly available priors for forthcoming investigations https://github.com/frgsimpson/neutrino-priors Conclusions with regard to the hierarchy are unchanged: we show that current data robustly prefer the normal hierarchy, by employing the least informative priors, with strong bayesian odds

neutrino: mass
neutrino: hierarchy
neutrino: oscillation
asymmetry
splitting
Bayesian

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