Latent-f and latent-H Gaussian process reconstructions from OHD data both yield f(z), w(z), and Om(z) consistent with Lambda-CDM, with no strong predictive preference and small prior-dependent residuals mainly at high redshift.
Nonparametric reconstruction of dynamical dark energy via observational Hubble parameter data
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abstract
We study the power of current and future observational Hubble parameter data (OHD) on non-parametric estimations of the dark energy equation of state, $w(z)$. We propose a new method by conjunction of principal component analysis (PCA) and the criterion of goodness of fit (GoF) criterion to reconstruct $w(z)$, ensuring the sensitivity and reliability of the extraction of features in the EoS. We also give an new error model to simulate future OHD data, to forecast the power of future OHD on the EoS reconstruction. The result shows that current OHD, despite in less quantity, give not only a similar power of reconstruction of dark energy compared to the result given by type Ia supernovae, but also extend the constraint on $w(z)$ up to redshift $z\simeq2$. Additionally, a reasonable forecast of future data in more quantity and better quality greatly enhances the reconstruction of dark energy.
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2026 1verdicts
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Latent-Space Gaussian Processes for Dark-Energy Reconstruction from Observational \(H(z)\) Data
Latent-f and latent-H Gaussian process reconstructions from OHD data both yield f(z), w(z), and Om(z) consistent with Lambda-CDM, with no strong predictive preference and small prior-dependent residuals mainly at high redshift.