Investigating the suitability of data-driven methods for extracting physical parameters in cosmological models
出版年份 2023 全文链接
标题
Investigating the suitability of data-driven methods for extracting physical parameters in cosmological models
作者
关键词
-
出版物
Astronomy and Computing
Volume -, Issue -, Pages 100762
出版商
Elsevier BV
发表日期
2023-11-03
DOI
10.1016/j.ascom.2023.100762
参考文献
相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。- Dark energy by natural evolution: Constraining dark energy using Approximate Bayesian Computation
- (2023) Reginald Christian Bernardo et al. Physics of the Dark Universe
- Hubble constant by natural selection: Evolution chips in the Hubble tension
- (2023) R.C. Bernardo et al. Astronomy and Computing
- Standardizing Platinum Dainotti-correlated gamma-ray bursts, and using them with standardized Amati-correlated gamma-ray bursts to constrain cosmological model parameters
- (2022) Shulei Cao et al. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
- Parametric and nonparametric methods hint dark energy evolution
- (2022) Reginald Christian Bernardo et al. Physics of the Dark Universe
- In the realm of the Hubble tension—a review of solutions *
- (2021) Eleonora Di Valentino et al. CLASSICAL AND QUANTUM GRAVITY
- A data-driven reconstruction of Horndeski gravity via the Gaussian processes
- (2021) Reginald Christian Bernardo et al. JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS
- Towards a model-independent reconstruction approach for late-time Hubble data
- (2021) Reginald Christian Bernardo et al. JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS
- Cosmographic Parameters in Model-independent Approaches
- (2021) Ahmad Mehrabi et al. ASTROPHYSICAL JOURNAL
- dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences
- (2020) Joshua S Speagle MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
- SciPy 1.0: fundamental algorithms for scientific computing in Python
- (2020) Pauli Virtanen et al. NATURE METHODS
- Array programming with NumPy
- (2020) Charles R. Harris et al. NATURE
- Deep learning approach to Hubble parameter
- (2020) H. Tilaver et al. COMPUTER PHYSICS COMMUNICATIONS
- Planck 2018 results. I. Overview and the cosmological legacy of Planck
- (2019) et al. ASTRONOMY & ASTROPHYSICS
- A new perspective on dark energy modeling via genetic algorithms
- (2012) Savvas Nesseris et al. JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS
- Genetic algorithms and supernovae type Ia analysis
- (2009) Charalampos Bogdanos et al. JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS
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