Extracting the 21-cm Power Spectrum and the reionization parameters from mock datasets using Artificial Neural Networks

Abstract

Detection of the \hi~ 21-cm power spectrum is one of the key science drivers of several ongoing and upcoming low-frequency radio interferometers. However, the major challenge in such observations come from bright foregrounds, whose accurate removal or avoidance is key to the success of these experiments. In this work, we demonstrate the use of artificial neural networks (ANNs) to extract the \hi~ 21-cm power spectrum from synthetic datasets and extract the reionization parameters from the \hi~ 21-cm power spectrum. For the first time, using a suite of simulations, we present an ANN based framework capable of extracting the \hi~ signal power spectrum directly from the total observed sky power spectrum (which contains the 21-cm signal, along with the foregrounds and effects of the instrument). To achieve this, we have used a combination of two separate neural networks sequentially. As the first step, \texttt{ANN1} predicts the 21-cm power spectrum directly from foreground corrupted synthetic datasets. In the second step, \texttt{ANN2} predicts the reionization parameters from the predicted \hi~ power spectra from \texttt{ANN1}. Our ANN-based framework is trained at a redshift of $9.01$, and for \kk-modes in the range, $\rm{0.17<\kk<0.37~Mpc^{-1}}$. We have tested the network's performance with mock datasets that include foregrounds and are corrupted with thermal noise, corresponding to $1080$ hrs of observations of the \textsc{ska-1 low} and \textsc{hera}. Using our ANN framework, we are able to recover the \hi~ power spectra with an accuracy of $\approx 95-99\%$ for the different test sets. For the predicted astrophysical parameters, we have achieved an accuracy of $\approx 81-90\%$ and $\approx 50-60\%$ for the test sets corrupted with thermal noise corresponding to the \textsc{ska-1 low} and \textsc{hera}, respectively.

arXiv commentSubmitted to MNRAS, under review

References in corpus (60)

• LOFAR: The LOw-Frequency ARray
• The Murchison Widefield Array: the Square Kilometre Array Precursor at low radio frequencies
• An absorption profile centred at 78 megahertz in the sky-averaged spectrum
• 21-cm cosmology
• How small were the first cosmological objects?
• 21cmFAST: A Fast, Semi-Numerical Simulation of the High-Redshift 21-cm Signal
• Hydrogen Epoch of Reionization Array (HERA)
• Reionization and Cosmology with 21 cm Fluctuations
• The Precision Array for Probing the Epoch of Reionization: 8 Station Results
• 21 Centimeter Fluctuations from Cosmic Gas at High Redshifts
• Bright Source Subtraction Requirements For Redshifted 21 cm Measurements
• A simulation calibrated limit on the HI power spectrum from the GMRT Epoch of Reionization experiment
• Improved upper limits on the 21-cm signal power spectrum of neutral hydrogen at $\mathbf{z}\approx 9.1$ from LOFAR
• Multifrequency analysis of 21 cm fluctuations from the Era of Reionization
• Foreground simulations for the LOFAR - Epoch of Reionization Experiment
• PAPER-64 Constraints on Reionization: The 21cm Power Spectrum at z=8.4
• Inferring the astrophysics of reionization and cosmic dawn from galaxy luminosity functions and the 21-cm signal
• 21CMMC: an MCMC analysis tool enabling astrophysical parameter studies of the cosmic 21 cm signal
• Radio Foregrounds for the 21cm Tomography of the Neutral Intergalactic Medium at High Redshifts
• A Sensitivity and Array-Configuration Study for Measuring the Power Spectrum of 21cm Emission from Reionization
• Foreground Removal using FastICA: A Showcase of LOFAR-EoR
• The Baryon Acoustic Oscillation Broadband and Broad-beam Array: Design Overview and Sensitivity Forecasts
• On using visibility correlations to probe the HI distribution from the dark ages to the present epoch I: Formalism and the expected signal
• Probing the Dark Ages at Z~20: The SCI-HI 21 cm All-Sky Spectrum Experiment
• Fast Large Volume Simulations of the 21 cm Signal from the Reionization and pre-Reionization Epochs
• BIGHORNS - Broadband Instrument for Global HydrOgen ReioNisation Signal
• Power Spectrum Sensitivity and the Design of Epoch of Reionization Observatories
• Foregrounds for 21cm Observations of Neutral Gas at High Redshift
• CHIPS: The Cosmological HI Power Spectrum Estimator
• Statistical 21-cm Signal Separation via Gaussian Process Regression Analysis
• Quantifying the non-Gaussianity in the EoR 21-cm signal through bispectrum
• The 21 centimeter emission from the reionization epoch: extended and point source foregrounds
• The multi-frequency angular power spectrum of the epoch of reionization 21 cm signal
• The Evolution of 21-cm Structure (EOS): public, large-scale simulations of Cosmic Dawn and Reionization
• Deep learning from 21-cm tomography of the Cosmic Dawn and Reionization
• Non-parametric foreground subtraction for 21cm epoch of reionization experiments
• HI Fluctuations at Large Redshifts: I--Visibility correlation
• Observing the Reionization Epoch Through 21 Centimeter Radiation
• Analysing the 21cm signal from the Epoch of Reionization with artificial neural networks
• Emulation of reionization simulations for Bayesian inference of astrophysics parameters using neural networks
• On the use of semi-numerical simulations in predicting the 21-cm signal from the epoch of reionization
• Will point sources spoil 21 cm tomography?
• Emulating the Global 21-cm Signal from Cosmic Dawn and Reionization
• Simulation-Based Inference of Reionization Parameters From 3D Tomographic 21 cm Lightcone Images
• Reionization Through the Lens of Percolation Theory
• Towards simulating and quantifying the light-cone EoR 21-cm signal
• Evaluating machine learning techniques for predicting power spectra from reionization simulations
• Identifying Reionization Sources from 21cm Maps using Convolutional Neural Networks
• Global 21-cm signal extraction from foreground and instrumental effects I: Pattern recognition framework for separation using training sets
• Statistics of the epoch of reionization 21-cm signal - I. Power spectrum error-covariance
• Separating the EoR Signal with a Convolutional Denoising Autoencoder: A Deep-learning-based Method
• Redshifted 21-cm Bispectrum I: Impact of the Redshift Space Distortions on the Signal from the Epoch of Reionization
• A deep learning model to emulate simulations of cosmic reionization
• Machine Learning Applied to the Reionization History of the Universe in the 21 cm Signal
• Redshifted 21-cm bispectrum II: Impact of the spin temperature fluctuations and redshift space distortions on the signal from the Cosmic Dawn
• Effects of the sources of reionization on 21-cm redshift-space distortions
• Joint estimation of the Epoch of Reionization power spectrum and foregrounds
• Detailed study of the ELAIS N1 field with the uGMRT - I. Characterizing the 325 MHz foreground for redshifted 21 cm observations
• Using Artificial Neural Networks to extract the 21-cm Global Signal from the EDGES data
• 21cm Global Signal Extraction: Extracting the 21cm Global Signal using Artificial Neural Networks

Cited by in corpus (12)

• Implicit Likelihood Inference of Reionization Parameters from the 21 cm Power Spectrum
• Machine Learning for Observational Cosmology
• Exploring the cosmic dawn and epoch of reionization with 21cm line
• Extracting the Global 21-cm signal from Cosmic Dawn and Epoch of Reionization in the presence of Foreground and Ionosphere
• Efficient exploration of reionization parameters for the upcoming 21 cm observations using a photon conserving semi-numerical model SCRIPT
• Comparing sampling techniques to chart parameter space of 21 cm Global signal with Artificial Neural Networks
• Probing the Epoch of Reionization using synergies of line intensity mapping
• Synthetic Observations with the Square Kilometre Array (SKA) -- development towards an end-to-end pipeline
• Impact of Calibration and Position Errors on Astrophysical Parameters of the HI 21cm Signal
• Deep Needlet: A CNN based full sky component separation method in Needlet space
• From ANN to BNN: Inferring Reionization Parameters using Uncertainty-aware Emulators of 21-cm Summaries
• Accelerating reionization constraints: An ANN-emulator framework for the SCRIPT Semi-numerical Model