Reconstruction of late-time cosmology using Principal Component Analysis

Abstract

We reconstruct late-time cosmology using the technique of Principal Component Analysis (PCA). In particular, we focus on the reconstruction of the dark energy equation of state from two different observational data-sets, Supernovae type Ia data, and Hubble parameter data. The analysis is carried out in two different approaches. The first one is a derived approach, where we reconstruct the observable quantity using PCA and subsequently construct the equation of state parameter. The other approach is the direct reconstruction of the equation of state from the data. A combination of PCA algorithm and calculation of correlation coefficients are used as prime tools of reconstruction. We carry out the analysis with simulated data as well as with real data. The derived approach is found to be statistically preferable over the direct approach. The reconstructed equation of state indicates a slowly varying equation of state of dark energy.

15 pages, 8 figures, accepted for publication in EPJP

References in corpus (17)

• Dynamics of dark energy
• Dark Energy and the Accelerating Universe
• Raising the bar: new constraints on the Hubble parameter with cosmic chronometers at z$\sim$2
• WMAP constraints on low redshift evolution of dark energy
• Gaussian Process Cosmography
• Nonparametric Dark Energy Reconstruction from Supernova Data
• The Dynamics of Quintessence, The Quintessence of Dynamics
• Reconstruction of the deceleration parameter and the equation of state of dark energy
• Mapping the Cosmological Expansion
• Power of Observational Hubble Parameter Data: a Figure of Merit Exploration
• Dynamical behavior of generic quintessence potentials: constraints on key dark energy observables
• Beware of commonly used approximations I: errors in forecasts
• Beware of commonly used approximations II: estimating systematic biases in the best-fit parameters
• A comparison of perturbations in fluid and scalar field models of dark energy
• Cosmic expansion history from SNe Ia data via information field theory -- the charm code
• Non-parametric modeling of the cosmological data, base on the ${\chi }^2$ distribution
• Nonparametric Methods in Astronomy: Think, Regress, Observe -- Pick Any Three

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