Implicit Likelihood Inference of the Neutrino Mass Hierarchy from Cosmological Data

Abstract

In this paper, we turn to the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline to perform a multi-round ILI of the neutrino mass hierarchy from cosmological data, including $TT$, $TE$, $EE$ power spectra of Planck 2018 and distance ratios of DESI DR2. More precisely, we first embed the CMB power spectra simulator $\mathtt{CLASS}$ into the LtU-ILI pipeline. And then, opting for Sequential Neural Likelihood Estimation (SNLE), we sequentially train neural networks using $6$ rounds of $10000$ simulations to target a ``black box'' likelihood of our forward model with one additional neutrino mass hierarchy parameter $\tildeΔ$ and six base cosmological parameters. We find $\tildeΔ=0.12^{+0.21}_{-0.23}~(68\%{\rm CL})$.