Reconstructing equation of state of dark energy with principal component analysis

Abstract

We represent a nonparametric method to reconstruct the equation of state for dark energy directly from observational Hubble parameter data. We use principal component analysis (PCA) to extract the signal from data with noise. Moreover, we modify Akaike information criterion (AIC) to guarantee the quality of reconstruction and avoid over-fitting simultaneously. The results show that our method is robust in reconstruction of dark energy equation of state. Although current observational Hubble parameter data alone can not give a strong constraint yet, our results indicate that future observations can significantly improve the quality of the reconstruction.

22 pages, 5 figures, Matches the published version in Physics of the Dark Universe. Journal reference: Physics of the Dark Universe, Volume 26, article id. 100379 (2019)

References in corpus (13)

• Wide-Field InfrarRed Survey Telescope-Astrophysics Focused Telescope Assets WFIRST-AFTA 2015 Report
• Reconstructing Dark Energy
• Baryon Acoustic Oscillations in the Ly-α forest of BOSS quasars
• Precision cosmology with Padé rational approximations: theoretical predictions versus observational limits
• Power of Observational Hubble Parameter Data: a Figure of Merit Exploration
• Examining the evidence for dynamical dark energy
• Comparison of dark energy models after Planck 2015
• Reconstruction of the Dark Energy equation of state
• Evidence for bouncing evolution before inflation after BICEP2
• Cosmological model-independent test of $\Lambda$CDM with two-point diagnostic by the observational Hubble parameter data
• Nonparametric reconstruction of dynamical dark energy via observational Hubble parameter data
• Testing backreaction effects with observational Hubble parameter data
• Direct reconstruction of dynamical dark energy from observational Hubble parameter data

Cited by in corpus (4)

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• Non-parametric reconstruction of cosmological observables using Gaussian Processes Regression
• Inference of cosmological models with principal component analysis