Influence of the Bounds of the Hyperparameters on the Reconstruction of Hubble Constant with Gaussian Process
arXiv:2105.12618 · doi:10.3847/1538-4357/ac05b8
Abstract
The cosmological model-independent method Gaussian process (GP) has been widely used in the reconstruction of Hubble constant , and the hyperparameters inside GP influence the reconstructed result derived from GP. Different hyperparameters inside GP are used in the constraint of derived from GP with observational Hubble parameter data (OHD), and the influence of the hyperparameters inside GP on the reconstruction of with GP is discussed. The discussion about the hyperparameters inside GP and the forecasts for future data show that the consideration of the lower and upper bounds on the GP's hyperparameters are necessary in order to get an extrapolated result of from GP reliably and robustly.
8 pages, 5 figures. Accepted for publication in ApJ
References in corpus (9)
- Raising the bar: new constraints on the Hubble parameter with cosmic chronometers at z2
- Baryon Acoustic Oscillations in the Lyα forest of BOSS DR11 quasars
- Cosmic Distances Calibrated to 1% Precision with Gaia EDR3 Parallaxes and Hubble Space Telescope Photometry of 75 Milky Way Cepheids Confirm Tension with LambdaCDM
- Baryon Acoustic Oscillations in the Ly-α forest of BOSS quasars
- Non-parametric spatial curvature inference using late-universe cosmological probes
- Power of Observational Hubble Parameter Data: a Figure of Merit Exploration
- Measurements of and reconstruction of the dark energy properties from a model-independent joint analysis
- Reaffirming the Cosmic Acceleration without Supernova and CMB
- Elucidating cosmological model dependence with
Cited by in corpus (22)
- Challenges for CDM: An update
- The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics
- On the evolution of the Hubble constant with the SNe Ia Pantheon Sample and Baryon Acoustic Oscillations: a feasibility study for GRB-cosmology in 2030
- Nonparametric late-time expansion history reconstruction and implications for the Hubble tension in light of recent DESI and type Ia supernovae data
- Neural Network Reconstruction of Late-Time Cosmology and Null Tests
- Calibrating Gamma-Ray Bursts by Using a Gaussian Process with Type Ia Supernovae
- Reconstruction of the dark sectors' interaction: A model-independent inference and forecast from GW standard sirens
- Effects of type Ia supernovae absolute magnitude priors on the Hubble constant value
- Gaussian Processes Reconstruction of the Dark Energy Potential
- Kernel Selection for Gaussian Process in Cosmology: with Approximate Bayesian Computation Rejection and Nested Sampling
- Exploring new physics in the late Universe's expansion through non-parametric inference
- Reconstructing the growth index with Gaussian Processes
- A Non-parametric Reconstruction of the Hubble Parameter Based on Radial Basis Function Neural Networks
- Constraints on Cosmological Models with Gamma-Ray Bursts in Cosmology-Independent Way
- Reconstruction of the dark energy scalar field potential by Gaussian process
- Unveiling the Universe with Emerging Cosmological Probes
- Gamma-Ray Bursts Calibrated from the Observational Data in Artificial Neural Network Framework
- Viability of general relativity and modified gravity cosmologies using high-redshift cosmic probes
- Probing the Cosmic Distance Duality Relation via Non-Parametric Reconstruction for High Redshifts
- Constraining the Hubble Constant with a Simulated Full Covariance Matrix Using Neural Networks
- Optimizing Gaussian Process Kernels Using Nested Sampling and ABC Rejection for H(z) Reconstruction
- Reconstruction of a dark energy model for the Dirac-Born-Infeld scalar field with the Hubble and DESI data via Gaussian process