COSMOPOWER: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys

Abstract

We present $CosmoPower$, a suite of neural cosmological power spectrum emulators providing orders-of-magnitude acceleration for parameter estimation from two-point statistics analyses of Large-Scale Structure (LSS) and Cosmic Microwave Background (CMB) surveys. The emulators replace the computation of matter and CMB power spectra from Boltzmann codes; thus, they do not need to be re-trained for different choices of astrophysical nuisance parameters or redshift distributions. The matter power spectrum emulation error is less than $0.4\%$ in the wavenumber range $k\in [10^{-5},10]{\mathrm{Mpc}}^{-1}$, for redshift $z\in [0,5]$. $CosmoPower$ emulates CMB temperature, polarisation and lensing potential power spectra in the $5\sigma$ region of parameter space around the $Planck$ best fit values with an error $\lesssim 10\%$ of the expected shot noise for the forthcoming Simons Observatory. $CosmoPower$ is showcased on a joint cosmic shear and galaxy clustering analysis from the Kilo-Degree Survey, as well as on a Stage IV $Euclid$-like simulated cosmic shear analysis. For the CMB case, $CosmoPower$ is tested on a $Planck$ 2018 CMB temperature and polarisation analysis. The emulators always recover the fiducial cosmological constraints with differences in the posteriors smaller than sampling noise, while providing a speed-up factor up to $O(10^4)$ to the complete inference pipeline. This acceleration allows posterior distributions to be recovered in just a few seconds, as we demonstrate in the $Planck$ likelihood case. $CosmoPower$ is written entirely in Python, can be interfaced with all commonly used cosmological samplers and is publicly available at https://github.com/alessiospuriomancini/cosmopower .

arXiv comment13+6 pages, 6+3 figures. Matches MNRAS published version. COSMOPOWER available at https://github.com/alessiospuriomancini/cosmopower

References in corpus (75)

• emcee: The MCMC Hammer
• Efficient Computation of CMB anisotropies in closed FRW models
• LSST: from Science Drivers to Reference Design and Anticipated Data Products
• MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics
• The Cosmic Linear Anisotropy Solving System (CLASS) II: Approximation schemes
• Stable clustering, the halo model and nonlinear cosmological power spectra
• X-ray spectral modelling of the AGN obscuring region in the CDFS: Bayesian model selection and catalogue
• The Simons Observatory: Science goals and forecasts
• Revising the Halofit Model for the Nonlinear Matter Power Spectrum
• Galaxy and Mass Assembly (GAMA): survey diagnostics and core data release
• Wide-Field InfrarRed Survey Telescope-Astrophysics Focused Telescope Assets WFIRST-AFTA 2015 Report
• KiDS-450: Cosmological parameter constraints from tomographic weak gravitational lensing
• KiDS-1000 Cosmology: Cosmic shear constraints and comparison between two point statistics
• KiDS-1000 Cosmology: Multi-probe weak gravitational lensing and spectroscopic galaxy clustering constraints
• Cobaya: Code for Bayesian Analysis of hierarchical physical models
• Galaxy And Mass Assembly (GAMA): end of survey report and data release 2
• PolyChord: nested sampling for cosmology
• PolyChord: next-generation nested sampling
• Extended Limber Approximation
• An accurate halo model for fitting non-linear cosmological power spectra and baryonic feedback models
• GAMA: towards a physical understanding of galaxy formation
• Curvature tension: evidence for a closed universe
• KiDS+VIKING-450: Cosmic shear tomography with optical+infrared data
• Massive Neutrinos and the Non-linear Matter Power Spectrum
• HMcode-2020: Improved modelling of non-linear cosmological power spectra with baryonic feedback
• The Coyote Universe I: Precision Determination of the Nonlinear Matter Power Spectrum
• Massive Lossless Data Compression and Multiple Parameter Estimation from Galaxy Spectra
• Accurate halo-model matter power spectra with dark energy, massive neutrinos and modified gravitational forces
• KiDS+GAMA: Cosmology constraints from a joint analysis of cosmic shear, galaxy-galaxy lensing and angular clustering
• B-modes in cosmic shear from source redshift clustering
• The Coyote Universe II: Cosmological Models and Precision Emulation of the Nonlinear Matter Power Spectrum
• The Coyote Universe Extended: Precision Emulation of the Matter Power Spectrum
• KiDS-450: The tomographic weak lensing power spectrum and constraints on cosmological parameters
• The BACCO Simulation Project: Exploiting the full power of large-scale structure for cosmology
• The Coyote Universe III: Simulation Suite and Precision Emulator for the Nonlinear Matter Power Spectrum
• Simultaneous measurement of cosmology and intrinsic alignments using joint cosmic shear and galaxy number density correlations
• Euclid preparation: II. The EuclidEmulator -- A tool to compute the cosmology dependence of the nonlinear matter power spectrum
• Modelling baryonic feedback for survey cosmology
• hi_class: Horndeski in the Cosmic Linear Anisotropy Solving System
• Euclid preparation: IX. EuclidEmulator2 -- Power spectrum emulation with massive neutrinos and self-consistent dark energy perturbations
• Constructing A Flexible Likelihood Function For Spectroscopic Inference
• The Mira-Titan Universe II: Matter Power Spectrum Emulation
• KiDS-1000 Methodology: Modelling and inference for joint weak gravitational lensing and spectroscopic galaxy clustering analysis
• Emulating Simulations of Cosmic Dawn for 21cm Power Spectrum Constraints on Cosmology, Reionization, and X-ray Heating
• Emulation of reionization simulations for Bayesian inference of astrophysics parameters using neural networks
• Gravitational Lensing of Distant Field Galaxies by Rich Clusters: II. -- Cluster Mass Distributions
• AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
• Fast cosmological parameter estimation using neural networks
• Quantifying the global parameter tensions between ACT, SPT and Planck
• SPECULATOR: Emulating stellar population synthesis for fast and accurate galaxy spectra and photometry
• On the complementarity of galaxy clustering with cosmic shear and flux magnification
• Bayesian emulator optimisation for cosmology: application to the Lyman-alpha forest
• {\sc CosmoNet}: fast cosmological parameter estimation in non-flat models using neural networks
• KiDS+GAMA: Constraints on Horndeski gravity from combined large-scale structure probes
• Efficient Cosmological Parameter Estimation with Hamiltonian Monte Carlo
• An Emulator for the Lyman-alpha Forest
• Matter Power Spectrum Emulator for f(R) Modified Gravity Cosmologies
• Euclid: The importance of galaxy clustering and weak lensing cross-correlations within the photometric Euclid survey
• GLOBALEMU: A novel and robust approach for emulating the sky-averaged 21-cm signal from the cosmic dawn and epoch of reionisation
• PkANN - II. A non-linear matter power spectrum interpolator developed using artificial neural networks
• PkANN - I. Non-linear matter power spectrum interpolation through artificial neural networks
• Fast Cosmological Parameter Estimation from Microwave Background Temperature and Polarization Power Spectra
• Data compression in cosmology: A compressed likelihood for Planck data
• Magnification bias in galaxy surveys with complex sample selection functions
• Kernel-Based Emulator for the 3D Matter Power Spectrum from CLASS
• Self-calibration of weak lensing systematic effects using combined two- and three-point statistics
• Euclid: The reduced shear approximation and magnification bias for Stage IV cosmic shear experiments
• Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Models
• Multi-Fidelity Emulation for the Matter Power Spectrum using Gaussian Processes
• CosmicNet I: Physics-driven implementation of neural networks within Boltzmann-Einstein solvers
• Euclid-era cosmology for everyone: Neural net assisted MCMC sampling for the joint 3x2 likelihood
• Parameter Inference for Weak Lensing using Gaussian Processes and MOPED
• Incorporating Astrochemistry into Molecular Line Modelling via Emulation
• Accelerating Bayesian microseismic event location with deep learning
• Pico: Parameters for the Impatient Cosmologist

Cited by in corpus (98)

• The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics
• The Atacama Cosmology Telescope: A Measurement of the DR6 CMB Lensing Power Spectrum and its Implications for Structure Growth
• DES Y3 + KiDS-1000: Consistent cosmology combining cosmic shear surveys
• Cosmological constraints from the tomographic cross-correlation of DESI Luminous Red Galaxies and Planck CMB lensing
• Cosmology From CMB Lensing and Delensed EE Power Spectra Using 2019-2020 SPT-3G Polarization Data
• The Early Dark Sector, the Hubble Tension, and the Swampland
• SPT-3G D1: CMB temperature and polarization power spectra and cosmology from 2019 and 2020 observations of the SPT-3G Main field
• Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation
• CONNECT: A neural network based framework for emulating cosmological observables and cosmological parameter inference
• CosmicNet II: Emulating extended cosmologies with efficient and accurate neural networks
• Fast and accurate predictions of the nonlinear matter power spectrum for general models of Dark Energy and Modified Gravity
• Modified gravity and massive neutrinos: constraints from the full shape analysis of BOSS galaxies and forecasts for Stage IV surveys
• CosmoPower-JAX: high-dimensional Bayesian inference with differentiable cosmological emulators
• Machine Learning for Observational Cosmology
• Capse.jl: efficient and auto-differentiable CMB power spectra emulation
• Projected-Field Kinetic Sunyaev-Zel'dovich Cross-Correlations: Halo Model and Forecasts
• KiDS-Legacy: Consistency of cosmic shear measurements and joint cosmological constraints with external probes
• KiDS-1000 cosmology: Combined second- and third-order shear statistics
• KiDS-1000 Cosmology: Constraints from density split statistics
• candl: Cosmic Microwave Background Analysis with a Differentiable Likelihood
• Profile Likelihoods in Cosmology: When, Why and How illustrated with $\Lambda$CDM, Massive Neutrinos and Dark Energy
• A roadmap to cosmological parameter analysis with third-order shear statistics I: Modelling and validation
• Retrieving cosmological information from small-scale CMB foregrounds II. The kinetic Sunyaev Zel'dovich effect
• Analysis of Unified Galaxy Power Spectrum Multipole Measurements
• Fast and robust Bayesian Inference using Gaussian Processes with GPry
• syren-halofit: A fast, interpretable, high-precision formula for the $\Lambda$CDM nonlinear matter power spectrum
• Neural Networks Optimized by Genetic Algorithms in Cosmology
• The Aemulus Project VI: Emulation of beyond-standard galaxy clustering statistics to improve cosmological constraints
• LINNA: Likelihood Inference Neural Network Accelerator
• A precise symbolic emulator of the linear matter power spectrum
• $\text{matryoshka}$ II: Accelerating Effective Field Theory Analyses of the Galaxy Power Spectrum
• Evolution mapping: a new approach to describe matter clustering in the non-linear regime
• Cosmology-informed neural networks to solve the background dynamics of the Universe
• Cosmology from the integrated shear 3-point correlation function: simulated likelihood analyses with machine-learning emulators
• Asteroseismology of $\delta$ Scuti stars: emulating model grids using a neural network
• Accelerating cosmological inference with Gaussian processes and neural networks -- an application to LSST Y1 weak lensing and galaxy clustering
• syren-new: Precise formulae for the linear and nonlinear matter power spectra with massive neutrinos and dynamical dark energy
• Retrieving cosmological information from small-scale CMB foregrounds I. The thermal Sunyaev Zel'dovich effect
• DISCO-DJ I: a differentiable Einstein-Boltzmann solver for cosmology
• Fast and effortless computation of profile likelihoods using CONNECT
• A comparison of Bayesian sampling algorithms for high-dimensional particle physics and cosmology applications
• Interferometric HI intensity mapping: perturbation theory predictions and foreground removal effects
• Bayesian Control Variates for optimal covariance estimation with pairs of simulations and surrogates
• LINX: A Fast, Differentiable, and Extensible Big Bang Nucleosynthesis Package
• LimberJack.jl: auto-differentiable methods for angular power spectra analyses
• On the degeneracies between baryons, massive neutrinos and f(R) gravity in Stage IV cosmic shear analyses
• Cosmic Cartography: Bayesian reconstruction of the galaxy density informed by large-scale structure
• Fast likelihood-free inference in the LSS Stage IV era
• A representation learning approach to probe for dynamical dark energy in matter power spectra
• Detection of Dipole Modulation in CMB Temperature Anisotropy Maps from WMAP and Planck using Artificial Intelligence
• KiDS-Legacy: Covariance validation and the unified OneCovariance framework for projected large-scale structure observables
• Improved Modeling of the Kinematic Sunyaev-Zel'dovich Projected-Fields signal and its Cosmological Dependence
• A roadmap to cosmological parameter analysis with third-order shear statistics II: Analytic covariance estimate
• Towards fast machine-learning-assisted Bayesian posterior inference of microseismic event location and source mechanism
• A field-level emulator for modified gravity
• Deep Learning and genetic algorithms for cosmological Bayesian inference speed-up
• High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning
• Weak Lensing Trispectrum and Kurt-Spectra
• Full-waveform earthquake source inversion using simulation-based inference
• Estimation of Full Sky Power Spectrum between Intermediate to Large Angular Scales from Partial Sky CMB Anisotropies using Artificial Neural Network
• Euclid preparation LXXI. Simulations and nonlinearities beyond $\mathsf{\Lambda }$CDM. 3. Constraints on $f(R)$ models from the photometric primary probes
• Faster Bayesian inference with neural network bundles and new results for $f(R)$ models
• Prospects for disentangling dark matter with weak lensing
• MF-Box: Multi-fidelity and multi-scale emulation for the matter power spectrum
• Assessing theoretical uncertainties for cosmological constraints from weak lensing surveys
• Bayesian error propagation for neural-net based parameter inference
• Cosmological Inference with Cosmic Voids and Neural Network Emulators
• The Simons Observatory: impact of bandpass, polarization angle and calibration uncertainties on small-scale power spectrum analysis
• Calculating Bayesian evidence for inflationary models using CONNECT
• Misinterpreting Modified Gravity as Dark Energy: a Quantitative Study
• $\Lambda$CDM and early dark energy in latent space: a data-driven parametrization of the CMB temperature power spectrum
• ParamANN: A Neural Network to Estimate Cosmological Parameters for $\Lambda$CDM Universe Using Hubble Measurements
• Stellar halos tracing the assembly of ultra-faint dwarf galaxies
• Interpretable and physics-informed emulator for the linear matter power spectrum from machine learning
• DeepSSM: an emulator of gravitational wave spectra from sound waves during cosmological first-order phase transitions
• A Novel Bispectrum Estimator of the Kinematic Sunyaev-Zel'dovich Effect using Projected Fields
• Cosmic Kite: Auto-encoding the Cosmic Microwave Background
• KiDS-Legacy: Constraining dark energy, neutrino mass, and curvature
• Parameter inference with non-linear galaxy clustering: accounting for theoretical uncertainties
• 6x2pt: Forecasting gains from joint weak lensing and galaxy clustering analyses with spectroscopic-photometric galaxy cross-correlations
• Cosmological parameters from the joint analysis of Density Split and Second Order Statistics: an Emulator based on the Halo Occupation Distribution
• Reconstruction of full sky CMB $\mathbf{E}$ and $\mathbf{B}$ modes spectra removing $\mathbf{E}$-to-$\mathbf{B}$ leakage from partial sky using deep learning
• Constraining the primordial power spectrum using a differentiable likelihood
• Design and optimization of neural networks for multifidelity cosmological emulation
• Power Spectrum Emulators from Neural Networks and Tree-Based Methods
• Dusty Clump Survival in Supernova Ejecta: Dust-Mediated Growth vs. Crushing by the Reverse Shock
• Evolution of linear matter perturbations with error-bounded bundle physics-informed neural networks
• Anchors no more: Using peculiar velocities to constrain $H_0$ and the primordial Universe without calibrators
• Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum
• Constraining cosmological parameters using density split lensing and the conditional stellar mass function
• On the accuracy of posterior recovery with neural network emulators
• Running the small-correlated-against-large estimator at scale: Applications of small-scale CMB lensing estimators on realistic simulations
• How to embed any likelihood into SBI: Application to Planck + Stage IV galaxy surveys and Dynamical Dark Energy
• Web-Halo Model (WHM): Accurate non-linear matter power spectrum predictions without free parameters
• Conditional variational autoencoders for cosmological model discrimination and anomaly detection in cosmic microwave background power spectra
• ABCMB: A Python+JAX Package for the Cosmic Microwave Background Power Spectrum
• The Parameter-Level Performance of Covariance Matrix Conditioning in Cosmic Microwave Background Data Analyses
• Neural Network Acceleration of Large-scale Structure Theory Calculations