Using machine learning to compress the matter transfer function $T(k)$

Abstract

The linear matter power spectrum $P(k,z)$ connects theory with large scale structure observations in cosmology. Its scale dependence is entirely encoded in the matter transfer function $T(k)$, which can be computed numerically by Boltzmann solvers, and can also be computed semi-analytically by using fitting functions such as the well-known Bardeen-Bond-Kaiser-Szalay (BBKS) and Eisenstein-Hu (EH) formulae. However, both the BBKS and EH formulae have some significant drawbacks. On the one hand, although BBKS is a simple expression, it is only accurate up to $10\%$, which is well above the $1\%$ precision goal of forthcoming surveys. On the other hand, while EH is as accurate as required by upcoming experiments, it is a rather long and complicated expression. Here, we use the Genetic Algorithms (GAs), a particular machine learning technique, to derive simple and accurate fitting formulae for the transfer function $T(k)$. When the effects of massive neutrinos are also considered, our expression slightly improves over the EH formula, while being notably shorter in comparison.

arXiv comment12 pages, 7 figures, 2 tables. Changes match published version

References in corpus (61)

• Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant
• Measurements of Omega and Lambda from 42 High-Redshift Supernovae
• Efficient Computation of CMB anisotropies in closed FRW models
• Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
• Type Ia Supernova Discoveries at z>1 From the Hubble Space Telescope: Evidence for Past Deceleration and Constraints on Dark Energy Evolution
• Modified Gravity and Cosmology
• The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: cosmological analysis of the DR12 galaxy sample
• A Flat Universe from High-Resolution Maps of the Cosmic Microwave Background Radiation
• Machine learning and the physical sciences
• Planck 2018 results. X. Constraints on inflation
• The Cosmic Linear Anisotropy Solving System (CLASS) II: Approximation schemes
• Baryonic Features in the Matter Transfer Function
• Stable clustering, the halo model and nonlinear cosmological power spectra
• In the Realm of the Hubble tension $-$ a Review of Solutions
• Large Magellanic Cloud Cepheid Standards Provide a 1% Foundation for the Determination of the Hubble Constant and Stronger Evidence for Physics Beyond LambdaCDM
• Baryon Acoustic Oscillations in the Sloan Digital Sky Survey Data Release 7 Galaxy Sample
• The 3D power spectrum of galaxies from the SDSS
• Dark Energy Survey Year 3 Results: Cosmological Constraints from Galaxy Clustering and Weak Lensing
• The WiggleZ Dark Energy Survey: mapping the distance-redshift relation with baryon acoustic oscillations
• Challenges for $\Lambda$CDM: An update
• Power Spectra for Cold Dark Matter and its Variants
• Cosmological implications of baryon acoustic oscillation (BAO) measurements
• Dark Energy Survey Year 1 Results: Cosmological Constraints from Cosmic Shear
• Planck 2018 results. VII. Isotropy and Statistics of the CMB
• Cosmological Constraints from the Clustering of the Sloan Digital Sky Survey DR7 Luminous Red Galaxies
• Dark Energy vs. Modified Gravity
• Dark Energy Survey Year 1 Results: Constraints on Extended Cosmological Models from Galaxy Clustering and Weak Lensing
• No evidence for extensions to the standard cosmological model
• Can the $H_0$ tension be resolved in extensions to $\Lambda$CDM cosmology?
• Matter power spectrum: from Ly$\alpha$ forest to CMB scales
• Curvature vs Distances: testing the FLRW cosmology
• Cosmological Constraints from Multiple Probes in the Dark Energy Survey
• A new perspective on Dark Energy modeling via Genetic Algorithms
• A Profile Likelihood Analysis of the Constrained MSSM with Genetic Algorithms
• A model independent null test on the cosmological constant
• What can Machine Learning tell us about the background expansion of the Universe?
• Euclid: Forecast constraints on the cosmic distance duality relation with complementary external probes
• Genetic Algorithms and Supernovae Type Ia Analysis
• A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters
• BAO+BBN revisited -- Growing the Hubble tension with a 0.7km/s/Mpc constraint
• Machine-learning hidden symmetries
• Novel null tests for the spatial curvature and homogeneity of the Universe and their machine learning reconstructions
• The Road to Precision Cosmology
• Genetic Algorithms and Experimental Discrimination of SUSY Models
• Comparative analysis of model-independent methods for exploring the nature of dark energy
• Gravitational Lensing and the Power Spectrum of Dark Matter Substructure: Insights from the ETHOS N-body Simulations
• Hints of dark energy anisotropic stress using Machine Learning
• Cosmology in scalar-vector-tensor theories
• Machine learning constraints on deviations from general relativity from the large scale structure of the Universe
• Machine Learning improved fits of the sound horizon at the baryon drag epoch
• Proca in the sky
• Machine learning forecasts of the cosmic distance duality relation with strongly lensed gravitational wave events
• Euclid: constraining dark energy coupled to electromagnetism using astrophysical and laboratory data
• Euclid: Forecast constraints on consistency tests of the $\Lambda$CDM model
• A Genetic Algorithm for Astroparticle Physics Studies
• Testing the $\Lambda$CDM paradigm with growth rate data and machine learning
• Strongly lensed supernovae as a self-sufficient probe of the distance duality relation
• Holographic energy density, dark energy sound speed, and tensions in cosmological parameters: $H_0$ and $S_8$
• Recent Results from the MAXIMA Experiment
• Cosmological constraints with the Effective Fluid approach for Modified Gravity
• Euclid: Testing the Copernican principle with next-generation surveys

Cited by in corpus (9)

• syren-halofit: A fast, interpretable, high-precision formula for the $\Lambda$CDM nonlinear matter power spectrum
• A precise symbolic emulator of the linear matter power spectrum
• Searching for local features in primordial power spectrum using genetic algorithms
• Machine learning unveils the linear matter power spectrum of modified gravity
• Perturbations of Cosmological Redshift Drift
• Interpretable and physics-informed emulator for the linear matter power spectrum from machine learning
• Forecast constraints on null tests of the $\Lambda$CDM model with SPHEREx
• Spatially resolved stellar-to-total dynamical mass relation: Radial variations, gradients and profiles of galaxy stellar populations
• A Quantum Genetic Algorithm with application to Cosmological Parameters Estimation