Estimation of Full Sky Power Spectrum between Intermediate to Large Angular Scales from Partial Sky CMB Anisotropies using Artificial Neural Network

Abstract

Reliable extraction of cosmological information from observed cosmic microwave background (CMB) maps may require removal of strongly foreground contaminated regions from the analysis. In this article, we employ an artificial neural network (ANN) to predict the full sky CMB angular power spectrum between intermediate to large angular scales from the partial sky spectrum obtained from masked CMB temperature anisotropy map. We use a simple ANN architecture with one hidden layer containing $895$ neurons. Using $1.2\times 10^5$ training samples of full sky and corresponding partial sky CMB angular power spectra at Healpix pixel resolution parameter $N_{side}=256$, we show that predicted spectrum by our ANN agrees well with the target spectrum at each realization for the multipole range $2\le l\le 512$. The predicted spectra are statistically unbiased and they preserve the cosmic variance accurately. Statistically, the differences between the mean predicted and underlying theoretical spectra are within approximately $3\sigma$. Moreover, the probability densities obtained from predicted angular power spectra agree very well with those obtained from `actual' full sky CMB angular power spectra for each multipole. Interestingly, our work shows that the significant correlations in input cut-sky spectra, due to mode-mode coupling introduced on the partial sky, are effectively removed since the ANN learns the hidden pattern between the partial sky and full sky spectra preserving the entire statistical properties. The excellent agreement of statistical properties between the predicted and the ground-truth demonstrates the importance of using artificial intelligence systems in cosmological analysis more widely.

arXiv comment17 pages, 15 figures

References in corpus (37)

• HEALPix -- a Framework for High Resolution Discretization, and Fast Analysis of Data Distributed on the Sphere
• Nine-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Cosmological Parameter Results
• Non-Gaussian features of primordial fluctuations in single field inflationary models
• Nine-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Final Maps and Results
• The Cosmic Microwave Background Spectrum from the Full COBE/FIRAS Data Set
• 4-Year COBE DMR Cosmic Microwave Background Observations: Maps and Basic Results
• MASTER of the CMB Anisotropy Power Spectrum: A Fast Method for Statistical Analysis of Large and Complex CMB Data Sets
• Second-Order Cosmological Perturbations from Inflation
• Calibrator Design for the COBE Far Infrared Absolute Spectrophotometer (FIRAS)
• The Three--Point Correlation Function of the Cosmic Microwave Background in Inflationary Models
• Estimating the Power Spectrum of the Cosmic Microwave Background
• Planck 2018 results. IV. Diffuse component separation
• The Atacama Cosmology Telescope: Cosmological parameters from three seasons of data
• Constraints on Cosmology from the Cosmic Microwave Background Power Spectrum of the 2500 ${\mathrm{deg}}^2$ SPT-SZ Survey
• Power Spectrum of Primordial Inhomogeneity Determined from the 4-Year COBE DMR Sky Maps
• Power spectrum estimation from high-resolution maps by Gibbs sampling
• A Measurement of the Angular Power Spectrum of the Anisotropy in the Cosmic Microwave Background
• COSMOPOWER: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys
• The Pseudo-$C_l$ method: Cosmic microwave background anisotropy power spectrum statistics for high precision cosmology
• Cosmic microwave background anisotropy power spectrum statistics for high precision cosmology
• LiteBIRD: JAXA's new strategic L-class mission for all-sky surveys of cosmic microwave background polarization
• On Determining the Spectrum of Primordial Inhomogeneity from the COBE DMR Sky Maps: II. Results of Two Year Data Analysis
• On Determining the Spectrum of Primordial Inhomogeneity from the COBE DMR Sky Maps: I. Method
• Neural networks and separation of Cosmic Microwave Background and astrophysical signals in sky maps
• A deep learning approach to cosmological dark energy models
• Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with the Hubble Parameter and SNe Ia
• Libsharp - spherical harmonic transforms revisited
• Hierarchical Cosmic Shear Power Spectrum Inference
• Neural Network Reconstruction of Late-Time Cosmology and Null Tests
• Full-sky Cosmic Microwave Background Foreground Cleaning Using Machine Learning
• Gabor Transforms on the Sphere with Applications to CMB Power Spectrum Estimation
• Unbiased pseudo-Cl power spectrum estimation with mode projection
• Fast, exact CMB power spectrum estimation for a certain class of observational strategies
• BAMBI: blind accelerated multimodal Bayesian inference
• Accelerated Bayesian inference using deep learning
• Dependence of Density Perturbations on the Coupling Constant in a Simple Model of Inflation
• An Unbiased Estimator of the Full-sky CMB Angular Power Spectrum at Large Scales using Neural Networks

Cited by in corpus (5)

• In search of an interaction in the dark sector through Gaussian Process and ANN approaches
• Detection of Dipole Modulation in CMB Temperature Anisotropy Maps from WMAP and Planck using Artificial Intelligence
• DeepSSM: an emulator of gravitational wave spectra from sound waves during cosmological first-order phase transitions
• ParamANN: A Neural Network to Estimate Cosmological Parameters for $\Lambda$CDM Universe Using Hubble Measurements
• 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