Nonparametric Variational Inference Reconstruction of the Cosmic Expansion History from SNe Ia -- the charm2 code

TL;DR

This paper introduces charm2, a nonparametric Bayesian reconstruction method using information field theory to analyze cosmic expansion history from supernova data, revealing potential signals of evolving dark energy.

Contribution

The work presents charm2, an improved IFT-based algorithm employing geometric variational inference for nonparametric cosmic energy density reconstruction.

Findings

Reconstruction results are consistent with flat LCDM for some data sets.

Deviations suggesting evolving dark energy are found in DESY5 data.

No conclusive evidence favors non-flat LCDM features based on ELBO measures.

Abstract

Cosmological analyses using the latest set of type Ia SNe data weakly favor an evolving dark energy (EDE) model without strongly disfavoring the standard LCDM paradigm. Nonparametric reconstructions of the expansion history may reveal signal features potentially missed by a parametric LCDM model without laying out a specific functional form for the evolution of dark energy. Information field theory (IFT) is a Bayesian framework for optimal, nonparametric reconstruction algorithms. In this work, we present charm2, the successor to charm1, a previous IFT-based code to reconstruct the cosmic energy density's redshift evolution from SNe Ia. We apply our reconstruction algorithm to the Union2.1, Pantheon+, DESY5 and DESY5-Dovekie data sets to investigate the agreement between the nonparametric reconstruction and the signal suggested by a parametric, flat LCDM model. To enable an accurate Gaussian approximation, we employ geometric variational inference, which finds a coordinate transformation through which a curved posterior gets "flattened". The redshift evolution of the energy density can then be traced on a double-logarithmic scale, which, after de-trending, is well described by a stationary Gaussian process. The nonparametric charm2 reconstructions using the Union2.1 and Pantheon+ data sets are consistent with flat LCDM signal fields. The DESY5 and DESY5-Dovekie reconstructions deviate from flat LCDM comparison fields and are compatible with an evolving dark energy signal. However, using the evidence lower bound (ELBO) measure for model selection, we find no conclusive evidence supporting a preference for non-flat-LCDM features in any of the data sets. We note that at current DESY5 noise levels, the ELBO tends to favor flat LCDM over our nonparametric model although the latter better recovers the ground truth in synthetic EDE data; a trend reversing only at ~7x lower noise covariance.