Deep-Learning Study of the 21cm Differential Brightness Temperature During the Epoch of Reionization

Abstract

We propose a deep learning analyzing technique with convolutional neural network (CNN) to predict the evolutionary track of the Epoch of Reionization (EoR) from the 21-cm differential brightness temperature tomography images. We use 21cmFAST, a fast semi-numerical cosmological 21-cm signal simulator, to produce mock 21-cm maps between $z=6\sim 13$. We then apply two observational effects into those 21-cm maps, such as instrumental noise and limit of (spatial and depth) resolution somewhat suitable for realistic choices of the Square Kilometre Array (SKA). We design our deep learning model with CNN to predict the sliced-averaged neutral hydrogen fraction from the given 21-cm map. The estimated neutral fraction from our CNN model has a great agreement with its true value even after coarsely smoothing with broad beamsize and frequency bandwidth, and also heavily covered by noise with narrow. Our results have shown that deep learning analyzing method has a large potential to efficiently reconstruct the EoR history from the 21-cm tomography surveys in future.

arXiv comment25 pages, 8 figures, added references

References in corpus (30)

• LOFAR: The LOw-Frequency ARray
• Cosmology at Low Frequencies: The 21 cm Transition and the High-Redshift Universe
• Constraining the Evolution of the Ionizing Background and the Epoch of Reionization with z ~ 6 Quasars II: A Sample of 19 Quasars
• The Murchison Widefield Array: the Square Kilometre Array Precursor at low radio frequencies
• 21cmFAST: A Fast, Semi-Numerical Simulation of the High-Redshift 21-cm Signal
• Hydrogen Epoch of Reionization Array (HERA)
• The Growth of HII Regions During Reionization
• 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
• Inferring the astrophysics of reionization and cosmic dawn from galaxy luminosity functions and the 21-cm signal
• Taxing the Rich: Recombinations and Bubble Growth During Reionization
• Radio Foregrounds for the 21cm Tomography of the Neutral Intergalactic Medium at High Redshifts
• Simulating Cosmic Reionization at Large Scales II: the 21-cm Emission Features and Statistical Signals
• The GMRT-EoR Experiment: A new upper limit on the neutral hydrogen power spectrum at z~8.6
• Twenty-one centimeter tomography with foregrounds
• Cosmic microwave background constraints on the duration and timing of reionization from the South Pole Telescope
• Detecting the Rise and Fall of the First Stars by Their Impact on Cosmic Reionization
• The Dark Age of the Universe
• Nonlinear clustering during the cosmic Dark Ages and its effect on the 21-cm background from minihalos
• Model-independent constraints on reionization from large-scale CMB polarization
• Analysing the 21cm signal from the Epoch of Reionization with artificial neural networks
• Deep Convolutional Neural Networks as strong gravitational lens detectors
• Constraining cosmology and ionization history with combined 21 cm power spectrum and global signal measurements
• Complete Reionization Constraints from Planck 2015 Polarization
• 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
• The Escape Fraction of Ionizing Photons During the Epoch of Reionization: observability with the Square Kilometre Array
• A unified framework for 21cm tomography sample generation and parameter inference with Progressively Growing GANs
• Two-dimensional Topology of Cosmological Reionization

Cited by in corpus (14)

• deep21: a Deep Learning Method for 21cm Foreground Removal
• Machine learning astrophysics from 21 cm lightcones: impact of network architectures and signal contamination
• Lossless, Scalable Implicit Likelihood Inference for Cosmological Fields
• Removing Astrophysics in 21 cm maps with Neural Networks
• Exploring the cosmic 21-cm signal from the Epoch of Reionisation using the Wavelet Scattering Transform
• HInet: Generating neutral hydrogen from dark matter with neural networks
• How informative are summaries of the cosmic 21-cm signal?
• Exploring the cosmic dawn and epoch of reionization with 21cm line
• Detecting the non-Gaussianity of the 21-cm signal during reionisation with the Wavelet Scattering Transform
• Eliminating polarization leakage effect for neutral hydrogen intensity mapping with deep learning
• Understanding the Impact of Semi-numeric Reionization Models when using CNNs
• Probing Ultra-light Axion Dark Matter from 21cm Tomography using Convolutional Neural Networks
• Exploring the Model Dependence of MCMC-Based 21 cm Power Spectrum Parameter Constraints
• Accelerating reionization constraints: An ANN-emulator framework for the SCRIPT Semi-numerical Model