Simulation-based inference with seven dark-energy bins yields w0 = -0.90 ± 0.05, a marginal ~2σ preference for w > -1 at low redshift, while all other constrained bins agree with ΛCDM.
Implicit Likelihood Inference of the Neutrino Mass Hierarchy from Cosmological Data
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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 $\tilde{\Delta}$ and six base cosmological parameters. We find $\tilde{\Delta}=0.12^{+0.21}_{-0.23}~(68\%{\rm CL})$.
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Implicit Likelihood Inference and $z$-Binned Reconstruction of Dark Energy $w(z)$
Simulation-based inference with seven dark-energy bins yields w0 = -0.90 ± 0.05, a marginal ~2σ preference for w > -1 at low redshift, while all other constrained bins agree with ΛCDM.