Kernel dependence of the Gaussian Process reconstruction of late Universe expansion history
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912569839583232 |
|---|---|
| author | Johnson, Joseph P Jassal, H. K. |
| author_facet | Johnson, Joseph P Jassal, H. K. |
| contents | In this work, we discuss model-independent reconstruction of the expansion history of the late Universe. We use Gaussian Process Regression (GPR) to reconstruct the evolution of various cosmological parameters such as Hubble parameter $H(z)$ and deceleration parameter $q(z)$ using observational data to train the GPR model. We look at the GP reconstruction of these parameters using stationary and non-stationary kernel functions. We examine the effect of the choice of kernel functions on the reconstructions. We find that using non-stationary kernels such as lower-order polynomial kernels is a better choice for the reconstruction if the training data set is noisy (such as $H(z)$ data) as shown by the log marginal likelihood analysis. We also look at the reconstructions of the derivatives of $H(z)$ and study the kernel dependence on the reconstruction other cosmological parameters such as the $q(z)$ and the redshift of transition to the accelerated expansion. We see that reconstructed evolution of $q(z)$ also indicate that lower-order polynomial kernels are a better choice for the reconstruction compared to the stationary kernels. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04273 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Kernel dependence of the Gaussian Process reconstruction of late Universe expansion history Johnson, Joseph P Jassal, H. K. Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology In this work, we discuss model-independent reconstruction of the expansion history of the late Universe. We use Gaussian Process Regression (GPR) to reconstruct the evolution of various cosmological parameters such as Hubble parameter $H(z)$ and deceleration parameter $q(z)$ using observational data to train the GPR model. We look at the GP reconstruction of these parameters using stationary and non-stationary kernel functions. We examine the effect of the choice of kernel functions on the reconstructions. We find that using non-stationary kernels such as lower-order polynomial kernels is a better choice for the reconstruction if the training data set is noisy (such as $H(z)$ data) as shown by the log marginal likelihood analysis. We also look at the reconstructions of the derivatives of $H(z)$ and study the kernel dependence on the reconstruction other cosmological parameters such as the $q(z)$ and the redshift of transition to the accelerated expansion. We see that reconstructed evolution of $q(z)$ also indicate that lower-order polynomial kernels are a better choice for the reconstruction compared to the stationary kernels. |
| title | Kernel dependence of the Gaussian Process reconstruction of late Universe expansion history |
| topic | Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology |
| url | https://arxiv.org/abs/2503.04273 |