An Unbiased Estimator of the Full-sky CMB Angular Power Spectrum at Large Scales using Neural Networks

Abstract

Accurate estimation of the Cosmic Microwave Background (CMB) angular power spectrum is enticing due to the prospect for precision cosmology it presents. Galactic foreground emissions, however, contaminate the CMB signal and need to be subtracted reliably in order to lessen systematic errors on the CMB temperature estimates. Typically bright foregrounds in a region lead to further uncertainty in temperature estimates in the area even after some foreground removal technique is performed and hence determining the underlying full-sky angular power spectrum poses a challenge. We explore the feasibility of utilizing artificial neural networks to predict the angular power spectrum of the full sky CMB temperature maps from the observed angular power spectrum of the partial sky in which CMB temperatures in some bright foreground regions are masked. We present our analysis at large angular scales with two different masks. We produce unbiased predictions of the full-sky angular power spectrum and recover the underlying theoretical power spectrum using neural networks. Our predictions are also uncorrelated to a large extent. We further show that the multipole-space covariances of the predictions of full-sky spectra made by the ANNs are much smaller than those of the estimates obtained using the pseudo-$C_{\ell }$ method.

arXiv comment10 pages, 11 figures; altered methodology, added links to references, updated analysis using latest available data, modified the write-up accordingly

References in corpus (29)

• Adam: A Method for Stochastic Optimization
• HEALPix -- a Framework for High Resolution Discretization, and Fast Analysis of Data Distributed on the Sphere
• An overview of gradient descent optimization algorithms
• What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
• The Cosmic Microwave Background Spectrum from the Full COBE/FIRAS Data Set
• 4-Year COBE DMR Cosmic Microwave Background Observations: Maps and Basic Results
• First Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Foreground Emission
• MASTER of the CMB Anisotropy Power Spectrum: A Fast Method for Statistical Analysis of Large and Complex CMB Data Sets
• 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
• Joint Bayesian component separation and CMB power spectrum estimation
• Constraints on Cosmology from the Cosmic Microwave Background Power Spectrum of the 2500 ${\mathrm{deg}}^2$ SPT-SZ Survey
• Power spectrum estimation from high-resolution maps by Gibbs sampling
• 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
• Libsharp - spherical harmonic transforms revisited
• Hierarchical Cosmic Shear Power Spectrum Inference
• CMB anisotropy power spectrum using linear combinations of WMAP maps
• A blind estimation of the power spectrum of CMB anisotropy from WMAP
• Full-sky Cosmic Microwave Background Foreground Cleaning Using Machine Learning
• Gabor Transforms on the Sphere with Applications to CMB Power Spectrum Estimation
• Exploring Cosmic Origins with CORE: The Instrument
• Unbiased pseudo-Cl power spectrum estimation with mode projection
• Fast, exact CMB power spectrum estimation for a certain class of observational strategies
• Using inpainting to construct accurate cut-sky CMB estimators
• Concrete Dropout
• A Global ILC Approach in Pixel Space over Large Angular Scales of the Sky using CMB Covariance Matrix
• Inpainting CMB maps using Partial Convolutional Neural Networks

Cited by in corpus (6)

• Extracting cosmological parameters from N-body simulations using machine learning techniques
• Detection of Dipole Modulation in CMB Temperature Anisotropy Maps from WMAP and Planck using Artificial Intelligence
• Estimation of Full Sky Power Spectrum between Intermediate to Large Angular Scales from Partial Sky CMB Anisotropies using Artificial Neural Network
• Deep Needlet: A CNN based full sky component separation method in Needlet space
• 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