Defying the laws of Gravity I: model-independent reconstruction of the Universe expansion from growth data
arXiv:1906.05991 · doi:10.1093/mnras/staa633
Abstract
Using redshift space distortion data, we perform model-independent reconstructions of the growth history of matter inhomogeneity in the expanding Universe using two methods: crossing statistics and Gaussian processes. We then reconstruct the corresponding history of the Universe background expansion and fit it to type Ia supernovae data, putting constraints on . The results obtained are consistent with the concordance flat-CDM model and General Relativity as the gravity theory given the current quality of the inhomogeneity growth data.
8 pages, 7 figures. v3: match published version
References in corpus (13)
- Dynamics of dark energy
- Reconstructing Dark Energy
- Gaussian Process Cosmography
- The Clustering of the SDSS Main Galaxy Sample II: Mock galaxy catalogues and a measurement of the growth of structure from Redshift Space Distortions at
- Theoretical Models of Dark Energy
- Nonparametric Dark Energy Reconstruction from Supernova Data
- Nonparametric Reconstruction of the Dark Energy Equation of State
- Non-parametric Star Formation History Reconstruction with Gaussian Processes I: Counting Major Episodes of Star Formation
- 2MTF VI. Measuring the velocity power spectrum
- Bouncing Universes in Scalar-Tensor Gravity Models admitting Negative Potentials
- Crossing Statistic: Reconstructing the Expansion History of the Universe
- Model Independent Expansion History from Supernovae: Cosmology versus Systematics
- When is the growth index constant?
Cited by in corpus (23)
- Challenges for CDM: An update
- DESI 2024: Reconstructing Dark Energy using Crossing Statistics with DESI DR1 BAO data
- Early modified gravity in light of the tension and LSS data
- Nonparametric late-time expansion history reconstruction and implications for the Hubble tension in light of recent DESI and type Ia supernovae data
- Quantifying the tension with the Redshift Space Distortion data set
- How to use GP: Effects of the mean function and hyperparameter selection on Gaussian Process regression
- Testing the effect of on tension using a Gaussian Process method
- Inferring and with cosmic growth rate measurements using machine learning
- Cosmology Intertwined: A Review of the Particle Physics, Astrophysics, and Cosmology Associated with the Cosmological Tensions and Anomalies
- Neural network reconstructions for the Hubble parameter, growth rate and distance modulus
- Reconstructing dark energy with model independent methods after DESI DR2 BAO
- Neural Networks Optimized by Genetic Algorithms in Cosmology
- Non-parametric reconstruction of cosmological observables using Gaussian Processes Regression
- Global properties of the growth index of matter inhomogeneities in the universe
- Using variability and VLBI to measure cosmological distances
- Reconstructing the growth index with Gaussian Processes
- Dark energy reconstruction analysis with artificial neural networks: Application on simulated Supernova Ia data from Rubin Observatory
- Observation of a very massive galaxy cluster at z=0.76 in SRG/eROSITA all-sky survey
- Perturbations in Tachyon Dark Energy and their Effect on Matter Clustering
- Is CDM a good model for the clumpy Universe?
- 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
- Data-driven modeling of rotation curves with artificial neural networks