Reconstructing Cosmological Initial Conditions from Late-Time Structure with Convolutional Neural Networks

Abstract

We present a method to reconstruct the initial linear-regime matter density field from the late-time non-linearly evolved density field in which we channel the output of standard first-order reconstruction to a convolutional neural network (CNN). Our method shows dramatic improvement over the reconstruction of either component alone. We show why CNNs are not well-suited for reconstructing the initial density directly from the late-time density: CNNs are local models, but the relationship between initial and late-time density is not local. Our method leverages standard reconstruction as a preprocessing step, which inverts bulk gravitational flows sourced over very large scales, transforming the residual reconstruction problem from long-range to local and making it ideally suited for a CNN. We develop additional techniques to account for redshift distortions, which warp the density fields measured by galaxy surveys. Our method improves the range of scales of high-fidelity reconstruction by a factor of 2 in wavenumber above standard reconstruction, corresponding to a factor of 8 increase in the number of well-reconstructed modes. In addition, our method almost completely eliminates the anisotropy caused by redshift distortions. As galaxy surveys continue to map the Universe in increasingly greater detail, our results demonstrate the opportunity offered by CNNs to untangle the non-linear clustering at intermediate scales more accurately than ever before.

arXiv commentPublished in Monthly Notices of the Royal Astronomical Society

References in corpus (43)

• The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: cosmological analysis of the DR12 galaxy sample
• The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: Baryon Acoustic Oscillations in the Data Release 10 and 11 galaxy samples
• Observational Probes of Cosmic Acceleration
• Non-Gaussianity from Inflation: Theory and Observations
• The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: Baryon Acoustic Oscillations in the Data Release 9 Spectroscopic Galaxy Sample
• Large-Scale Galaxy Bias
• A 2% Distance to z=0.35 by Reconstructing Baryon Acoustic Oscillations - I : Methods and Application to the Sloan Digital Sky Survey
• Redshift-Space Distortions, Pairwise Velocities and Nonlinearities
• Improving Cosmological Distance Measurements by Reconstruction of the Baryon Acoustic Peak
• Improved forecasts for the baryon acoustic oscillations and cosmological distance scale
• AbacusSummit: A Massive Set of High-Accuracy, High-Resolution $N$-Body Simulations
• Reconstruction of the early Universe as a convex optimization problem
• A reconstruction of the initial conditions of the Universe by optimal mass transportation
• Reconstructing Baryon Oscillations: A Lagrangian Theory Perspective
• High-precision predictions for the acoustic scale in the non-linear regime
• The $\text{Abacus}$ Cosmological $N$-body Code
• Reconstructing baryon oscillations
• The Abacus Cosmos: A Suite of Cosmological N-body Simulations
• A High-Fidelity Realization of the Euclid Code Comparison $N$-body Simulation with Abacus
• Iterative initial condition reconstruction
• Reconstruction within the Zeldovich approximation
• Galaxy Bias and its Effects on the Baryon Acoustic Oscillations Measurements
• Modeling the reconstructed BAO in Fourier space
• Cosmological Reconstruction From Galaxy Light: Neural Network Based Light-Matter Connection
• On the least action principle in cosmology
• Reconstruction of cosmological density and velocity fields in the Lagrangian Zel'dovich Approximation
• Reconstruction of primordial density fields
• Efficient Reconstruction of Linear Baryon Acoustic Oscillations in Galaxy Surveys
• An iterative reconstruction of cosmological initial density fields
• Reconstruction of cosmological initial conditions from galaxy redshift catalogues
• COSMIC BIRTH: Efficient Bayesian Inference of the Evolving Cosmic Web from Galaxy Surveys
• New method for initial density reconstruction
• Baryon acoustic oscillations reconstruction using convolutional neural networks
• A fast semi-discrete optimal transport algorithm for a unique reconstruction of the early Universe
• BAO reconstruction: a swift numerical action method for massive spectroscopic surveys
• Exploring the posterior surface of the large scale structure reconstruction
• Improving reconstruction of the baryon acoustic peak : the effect of local environment
• Reconstruction Analysis of Galaxy Redshift Surveys: A Hybrid Reconstruction Method
• Bayesian cosmic density field inference from redshift space dark matter maps
• The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: Effect of smoothing of density field on reconstruction and anisotropic BAO analysis
• Iterative removal of redshift space distortions from galaxy clustering
• Using Perturbative Least Action to Recover Cosmological Initial Conditions
• Reconstruction with velocities

Cited by in corpus (15)

• Cosmology inference at the field level from biased tracers in redshift-space
• Effective cosmic density field reconstruction with convolutional neural network
• Improving constraints on primordial non-Gaussianity using neural network based reconstruction
• Neural network reconstruction of density and velocity fields from the 2MASS Redshift Survey
• Learning the Universe: Learning to Optimize Cosmic Initial Conditions with Non-Differentiable Structure Formation Models
• Neural Network Reconstruction of Non-Gaussian Initial Conditions from Dark Matter Halos
• ParamANN: A Neural Network to Estimate Cosmological Parameters for $\Lambda$CDM Universe Using Hubble Measurements
• Lagrangian displacement field estimators in cosmology
• 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
• Map Reconstruction of radio observations with Conditional Invertible Neural Networks
• Lagrangian space remapping and the angular momentum reconstruction from cosmic structures
• From Redshift to Real Space: Combining Linear Theory With Neural Networks
• Fast Sampling of Cosmological Initial Conditions with Gaussian Neural Posterior Estimation
• On the Connection between Field-Level Inference and $n$-point Correlation Functions
• Searching optimal scales for reconstructing cosmological initial conditions using convolutional neural networks