Euclid-era cosmology for everyone: Neural net assisted MCMC sampling for the joint 3x2 likelihood

Abstract

We develop a fully non-invasive use of machine learning in order to enable open research on Euclid-sized data sets. Our algorithm leaves complete control over theory and data analysis, unlike many black-box like uses of machine learning. Focusing on a `3x2 analysis' which combines cosmic shear, galaxy clustering and tangential shear at a Euclid-like sky coverage, we arrange a total of 348000 data points into data matrices whose structure permits not only an easy prediction by neural nets, but it additionally permits the essential removal from the data of patterns which the neural nets could not `understand'. The latter provides an often lacking mechanism to control and debias the inference of physics. The theoretical backbone to our neural net training can be any conventional (deterministic) theory code, where we chose CLASS. After training, we infer the seven parameters of a $w$CDM cosmology by Monte Carlo Markov sampling posteriors at Euclid-like precision within a day. We publicly provide the neural nets which memorise and output all 3x2 power spectra at a Euclid-like sky coverage and redshift binning.

Original version of the authors, as accepted by the referee(s)

References in corpus (6)

• Euclid preparation: II. The EuclidEmulator -- A tool to compute the cosmology dependence of the nonlinear matter power spectrum
• Massive data compression for parameter-dependent covariance matrices
• On the insufficiency of arbitrarily precise covariance matrices: non-Gaussian weak lensing likelihoods
• The skewed weak lensing likelihood: why biases arise, despite data and theory being sound
• Properties and use of CMB power spectrum likelihoods
• Developing a unified pipeline for large-scale structure data analysis with angular power spectra -- I. The importance of redshift-space distortions for galaxy number counts

Cited by in corpus (23)

• COSMOPOWER: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys
• Cosmological constraints from the tomographic cross-correlation of DESI Luminous Red Galaxies and Planck CMB lensing
• Baryonic effects for weak lensing. Part I. Power spectrum and covariance matrix
• Parameters Estimation for the Cosmic Microwave Background with Bayesian Neural Networks
• CONNECT: A neural network based framework for emulating cosmological observables and cosmological parameter inference
• Cosmological parameter estimation via iterative emulation of likelihoods
• CosmicNet II: Emulating extended cosmologies with efficient and accurate neural networks
• Kernel-Based Emulator for the 3D Matter Power Spectrum from CLASS
• Capse.jl: efficient and auto-differentiable CMB power spectra emulation
• Deciphering the Archaeological Record: Cosmological Imprints of Non-Minimal Dark Sectors
• Parameter Inference for Weak Lensing using Gaussian Processes and MOPED
• Fast and robust Bayesian Inference using Gaussian Processes with GPry
• LINNA: Likelihood Inference Neural Network Accelerator
• Sufficiency of a Gaussian power spectrum likelihood for accurate cosmology from upcoming weak lensing surveys
• Accelerating cosmological inference with Gaussian processes and neural networks -- an application to LSST Y1 weak lensing and galaxy clustering
• Exact joint likelihood of pseudo-$C_{\ell }$ estimates from correlated Gaussian cosmological fields
• The impact of signal-to-noise, redshift, and angular range on the bias of weak lensing 2-point functions
• Bayesian error propagation for neural-net based parameter inference
• DeepSSM: an emulator of gravitational wave spectra from sound waves during cosmological first-order phase transitions
• Constraining the primordial power spectrum using a differentiable likelihood
• On the accuracy of posterior recovery with neural network emulators
• Neural Network Acceleration of Large-scale Structure Theory Calculations
• Constraining cosmological parameters using density split lensing and the conditional stellar mass function