Non-parametric reconstruction of cosmological observables using Gaussian Processes Regression

Abstract

The current accelerated expansion of the Universe remains ones of the most intriguing topics in modern cosmology, driving the search for innovative statistical techniques. Recent advancements in machine learning have significantly enhanced its application across various scientific fields, including physics, and particularly cosmology, where data analysis plays a crucial role in problem-solving. In this work, a non-parametric regression method with Gaussian processes is presented along with several applications to reconstruct some cosmological observables, such as the deceleration parameter and the dark energy equation of state, in order to contribute with some information that helps to clarify the behavior of the Universe. It was found that the results are consistent with $\Lambda$CDM and the predicted value of the Hubble parameter at redshift zero is $H_0=68.798\pm 6.340(1\sigma )\text{ km}{\text{ s}}^{-1}{\text{ Mpc}}^{-1}$.

21 pages, 19 figures, Universe 10 (2024) 12

References in corpus (92)

• Scikit-learn: Machine Learning in Python
• The Sloan Digital Sky Survey: Technical Summary
• Dynamics of dark energy
• The Supernova Legacy Survey: Measurement of Omega_M, Omega_Lambda and w from the First Year Data Set
• Overview of the DESI Legacy Imaging Surveys
• Statefinder -- a new geometrical diagnostic of dark energy
• The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Cosmological Implications from two Decades of Spectroscopic Surveys at the Apache Point observatory
• The Pantheon+ Analysis: Cosmological Constraints
• The Pantheon+ Analysis: The Full Dataset and Light-Curve Release
• Exploring the Expanding Universe and Dark Energy using the Statefinder Diagnostic
• Reconstructing Dark Energy
• GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
• The James Webb Space Telescope Mission
• Reconstruction of dark energy and expansion dynamics using Gaussian processes
• Gaussian Process Cosmography
• $H_0$ from cosmic chronometers and Type Ia supernovae, with Gaussian Processes and the novel Weighted Polynomial Regression method
• Eppur è piatto? The cosmic chronometer take on spatial curvature and cosmic concordance
• Non-parametric spatial curvature inference using late-universe cosmological probes
• An improved model-independent assessment of the late-time cosmic expansion
• Reconstructing the interaction between dark energy and dark matter using Gaussian Processes
• A parametric reconstruction of the deceleration parameter
• Nearest Neighbor distributions: new statistical measures for cosmological clustering
• Nonparametric Reconstruction of the Dark Energy Equation of State from Diverse Data Sets
• Reconstructing the dark sector interaction with LISA
• Paramerization of deceleration parameter in $f(Q)$ gravity
• Model-independent reconstruction of the Interacting Dark Energy Kernel: Binned and Gaussian process
• How to use GP: Effects of the mean function and hyperparameter selection on Gaussian Process regression
• Reconstruction of the Dark Energy equation of state
• Model-independent cosmological inference post DESI DR1 BAO measurements
• Gaussian processes reconstruction of dark energy from observational data
• Non-parametric reconstruction of the cosmological \textit{jerk} parameter
• Gaussian processes reconstruction of modified gravitational wave propagation
• PkANN - II. A non-linear matter power spectrum interpolator developed using artificial neural networks
• Testing the Mutual Consistency of the Pantheon and SDSS/eBOSS BAO Data Sets with Gaussian Processes
• Reconstructing teleparallel gravity with cosmic structure growth and expansion rate data
• CONNECT: A neural network based framework for emulating cosmological observables and cosmological parameter inference
• Assessment of the cosmic distance duality relation using Gaussian Process
• Quantifying the evidence for the current speed-up of the Universe with low and intermediate-redshift data. A more model-independent approach
• A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest
• PkANN - I. Non-linear matter power spectrum interpolation through artificial neural networks
• Reconstruction of the Dark Energy equation of state from latest data: the impact of theoretical priors
• Gravitational-Wave Detector Networks: Standard Sirens on Cosmology and Modified Gravity Theory
• Probing the cosmic distance duality relation using time delay lenses
• Constraining the dark energy equation of state using Bayes theorem and the Kullback-Leibler divergence
• A data-driven Reconstruction of Horndeski gravity via the Gaussian processes
• Revisiting a non-parametric reconstruction of the deceleration parameter from combined background and the growth rate data
• Model selection applied to reconstructions of the Dark Energy
• CosmicNet II: Emulating extended cosmologies with efficient and accurate neural networks
• Constraining the curvature density parameter in cosmology
• Neural network reconstructions for the Hubble parameter, growth rate and distance modulus
• Model independent bounds on Type Ia supernova absolute peak magnitude
• Non-parametric reconstruction of interaction in the cosmic dark sector
• Multi-Fidelity Emulation for the Matter Power Spectrum using Gaussian Processes
• Advanced Stationary and Non-Stationary Kernel Designs for Domain-Aware Gaussian Processes
• Neural Network Reconstruction of $H^{\prime }(z)$ and its application in Teleparallel Gravity
• Multivariate Classification with Random Forests for Gravitational Wave Searches of Black Hole Binary Coalescence
• Modeling Nearest Neighbor distributions of biased tracers using Hybrid Effective Field Theory
• Observational cosmology with Artificial Neural Networks
• Measurement on the cosmic curvature using the Gaussian process method
• Probing the cosmic opacity from Future Gravitational Wave Standard Sirens
• CosmicNet I: Physics-driven implementation of neural networks within Boltzmann-Einstein solvers
• Defying the laws of Gravity I: model-independent reconstruction of the Universe expansion from growth data
• Gaussian Process Approach for Model-Independent Reconstruction of $f(Q)$ Gravity with Direct Hubble Measurements
• A thorough investigation of the prospects of eLISA in addressing the Hubble tension: Fisher Forecast, MCMC and Machine Learning
• Non-parametric reconstruction of dark energy and cosmic expansion from the Pantheon compilation of type Ia supernovae
• Neural Networks Optimized by Genetic Algorithms in Cosmology
• Joint reconstructions of growth and expansion histories from stage-IV surveys with minimal assumptions. II. Modified gravity and massive neutrinos
• Remarks on the methods of investigations of alignment of galaxies
• Cosmology-informed neural networks to solve the background dynamics of the Universe
• LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring its Applications
• From Hubble to Snap Parameters: A Gaussian Process Reconstruction
• Investigation of the orientation of galaxies in clusters: the importance, methods and results of research
• Detection of spatial clustering in the 1000 richest SDSS DR8 redMaPPer clusters with Nearest Neighbor distributions
• Spatial Curvature and Thermodynamics
• Emulation of the Cosmic Dawn 21-cm Power Spectrum and Classification of Excess Radio Models Using an Artificial Neural Network
• 2D k-th nearest neighbor statistics: a highly informative probe of galaxy clustering
• Gaussian Process Classification for Galaxy Blend Identification in LSST
• Reconstructing the Hubble parameter with future Gravitational Wave missions using Machine Learning
• Minimal model dependent constraints on cosmological nuisance parameters and cosmic curvature from combinations of cosmological data
• Analytical Gaussian Process Cosmography: Unveiling Insights into Matter-Energy Density Parameter at Present
• Classification algorithms applied to structure formation simulations
• Testing the consistency between cosmological measurements of distance and age
• Checking the second law at cosmic scales
• A possible late-time transition of $M_B$ inferred via neural networks
• Reconstructing equation of state of dark energy with principal component analysis
• Deep Learning and genetic algorithms for cosmological Bayesian inference speed-up
• Examining the validity of the minimal varying speed of light model through cosmological observations: relaxing the null curvature constraint
• Analysis of Dark Matter Halo Structure Formation in $N$-body Simulations with Machine Learning
• Faster Bayesian inference with neural network bundles and new results for $f(R)$ models
• A comprehensive data-driven odyssey to explore the equation of state of dark energy
• Enhancing CMB map reconstruction and power spectrum estimation with convolutional neural networks
• Model-independent Reconstruction of UV Luminosity Function and Reionization Epoch

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• Derivative hierarchy as the origin of kernel-dependent trends in Gaussian process reconstructions of the hubble parameter
• Stable Islands of Weak Gravity
• A semi-cosmographic approach to study cosmological evolution in phase space
• Data-driven modeling of rotation curves with artificial neural networks
• Optimizing Gaussian Process Kernels Using Nested Sampling and ABC Rejection for H(z) Reconstruction
• Testing the cosmic distance duality relation using model-independent approach
• Model-independent reconstruction of cosmic thermodynamics and dark energy dynamics
• Graph-based Summary Statistics for Revealing the Stochastic Gravitational Wave Background in Pulsar Timing Arrays
• Variational autoencoder for generating realistic $N$-body simulations for dark matter halos