Machine Learning improved fits of the sound horizon at the baryon drag epoch

Abstract

The baryon acoustic oscillations (BAO) have proven to be an invaluable tool in constraining the expansion history of the Universe at late times and are characterized by the comoving sound horizon at the baryon drag epoch $r_{\mathrm{s}}(z_{\mathrm{d}})$. The latter quantity can be calculated either numerically using recombination codes or via fitting functions, such as the one by Eisenstein and Hu (EH), made via grids of parameters of the recombination history. Here we quantify the accuracy of these expressions and show that they can strongly bias the derived constraints on the cosmological parameters using BAO data. Then, using a machine learning approach, called the genetic algorithms, we proceed to derive new analytic expressions for $r_{\mathrm{s}}(z_{\mathrm{d}})$ which are accurate at the $\sim 0.003\%$ level in a range of $10\sigma$ around the Planck 2018 best-fit or $\sim 0.018\%$ in a much broader range, compared to $\sim 2-4\%$ for the EH expression, thus obtaining an improvement of two to three orders of magnitude. Moreover, we also provide fits that include the effects of massive neutrinos and an extension to the concordance cosmological model assuming variations of the fine structure constant. Finally, we note that our expressions can be used to ease the computational cost required to compute $r_{\mathrm{s}}(z_{\mathrm{d}})$ with a Boltzmann code when deriving cosmological constraints using BAO data from current and upcoming surveys.

arXiv comment8 pages, 2 figures, 2 tables. Changes match published version

References in corpus (48)

• Seven-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Cosmological Interpretation
• The 6dF Galaxy Survey: Baryon Acoustic Oscillations and the Local Hubble Constant
• Dark energy cosmology: the equivalent description via different theoretical models and cosmography tests
• The Cosmic Linear Anisotropy Solving System (CLASS) II: Approximation schemes
• Baryonic Features in the Matter Transfer Function
• The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: Baryon Acoustic Oscillations in the Data Release 10 and 11 galaxy samples
• Small Scale Cosmological Perturbations: An Analytic Approach
• Measuring the Baryon Acoustic Oscillation scale using the SDSS and 2dFGRS
• Cosmological implications of baryon acoustic oscillation (BAO) measurements
• Varying constants, Gravitation and Cosmology
• A New Calculation of the Recombination Epoch
• A Simple Varying-alpha Cosmology
• How Massless Neutrinos Affect the Cosmic Microwave Background Damping Tail
• HyRec: A fast and highly accurate primordial hydrogen and helium recombination code
• Baryon Acoustic Oscillations and the Hubble Constant: Past, Present and Future
• The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: measurement of the BAO and growth rate of structure of the emission line galaxy sample from the anisotropic power spectrum between redshift 0.6 and 1.1
• Scalar--Tensor Theories of Gravity with \PHI Dependent Masses
• Updated fundamental constant constraints from Planck 2018 data and possible relations to the Hubble tension
• Towards a complete treatment of the cosmological recombination problem
• A model-independent determination of the Hubble constant from lensed quasars and supernovae using Gaussian process regression
• Curvature vs Distances: testing the FLRW cosmology
• 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
• Breaking of the equivalence principle in the electromagnetic sector and its cosmological signatures
• Estimating the impact of recombination uncertainties on the cosmological parameter constraints from cosmic microwave background experiments
• Genetic Algorithms and Supernovae Type Ia Analysis
• A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters
• Robust and model-independent cosmological constraints from distance measurements
• Novel null tests for the spatial curvature and homogeneity of the Universe and their machine learning reconstructions
• HYREC-2: a highly accurate sub-millisecond recombination code
• Gaussian processes reconstruction of modified gravitational wave propagation
• Genetic Algorithms and Experimental Discrimination of SUSY Models
• Comparative analysis of model-independent methods for exploring the nature of dark energy
• Planck intermediate results. XXIV. Constraints on variation of fundamental constants
• Primordial nucleosynthesis with varying fundamental constants: Improved constraints and a possible solution to the Lithium problem
• Hints of dark energy anisotropic stress using Machine Learning
• Predicting dark matter halo formation in N-body simulations with deep regression networks
• Regression methods in waveform modeling: a comparative study
• Machine Learning meets the redshift evolution of the CMB Temperature
• Machine learning forecasts of the cosmic distance duality relation with strongly lensed gravitational wave events
• Constraining spatial variations of the fine structure constant using clusters of galaxies and Planck data
• Accuracy of cosmological parameters using the baryon acoustic scale
• Machine Learning and cosmographic reconstructions of quintessence and the Swampland conjectures
• Variations in fundamental constants at the cosmic dawn
• New Constraints on Spatial Variations of the Fine Structure Constant from Clusters of Galaxies

Cited by in corpus (26)

• The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics
• Constraining the dark energy models using Baryon Acoustic Oscillations: An approach independent of $H_0\cdot r_d$
• Euclid: Forecast constraints on consistency tests of the $\Lambda$CDM model
• Investigating the accelerated expansion of the Universe through updated constraints on viable $f(R)$ models within the metric formalism
• A thorough investigation of the prospects of eLISA in addressing the Hubble tension: Fisher Forecast, MCMC and Machine Learning
• A precise symbolic emulator of the linear matter power spectrum
• A null test of the Cosmological Principle with BAO measurements
• Dark energy reconstructions combining BAO data with galaxy clusters and intermediate redshift catalogs
• Testing the $\Lambda$CDM paradigm with growth rate data and machine learning
• A GREAT model comparison against the cosmological constant
• Using machine learning to compress the matter transfer function $T(k)$
• DE models with combined $H_0\cdot r_d$ from BAO and CMB dataset and friends
• The impact of the Universe's expansion rate on constraints on modified growth of structure
• $f(T,B)$ Gravity in the late Universe in the context of local measurements
• Searching for local features in primordial power spectrum using genetic algorithms
• Breaking the baryon-dark matter degeneracy in a model-independent way through the Sunyaev-Zeldovich effect
• Machine learning unveils the linear matter power spectrum of modified gravity
• Model-Independent Dark Energy Measurements from DESI DR2 and Planck 2015 Data
• Addressing the DESI DR2 Phantom-Crossing Anomaly and Enhanced $H_0$ Tension with Reconstructed Scalar-Tensor Gravity
• Is Chevallier-Polarski-Linder dark energy a mirage?
• Interpretable and physics-informed emulator for the linear matter power spectrum from machine learning
• Interacting dark sector with quadratic coupling: theoretical and observational viability
• Geometric acceleration in $f(Q,C)$ theories
• Forecast constraints on null tests of the $\Lambda$CDM model with SPHEREx
• A Spectrum of Cosmological Rips and Their Observational Signatures
• Genetic algorithm demystified for cosmological parameter estimation