Extracting the Global 21-cm signal from Cosmic Dawn and Epoch of Reionization in the presence of Foreground and Ionosphere

Abstract

Detection of redshifted \ion{H}{i} 21-cm emission is a potential probe for investigating the Universe's first billion years. However, given the significantly brighter foreground, detecting 21-cm is observationally difficult. The Earth's ionosphere considerably distorts the signal at low frequencies by introducing directional-dependent effects. Here, for the first time, we report the use of Artificial Neural Networks (ANNs) to extract the global 21cm signal characteristics from the composite all-sky averaged signal, including foreground and ionospheric effects such as refraction, absorption, and thermal emission from the ionosphere's F and D-layers. We assume a 'perfect' instrument and neglect instrumental calibration and beam effects. To model the ionospheric effect, we considered the static and time-varying ionospheric conditions for the mid-latitude region where LOFAR is situated. In this work, we trained the ANN model for various situations using a synthetic set of the global 21cm signals created by altering its parameter space based on the "$\tanh$" parameterized model and the Accelerated Reionization Era Simulations (ARES) algorithm. The obtained result shows that the ANN model can extract the global signal parameters with an accuracy of $\ge 96\%$ in the final study when we include foreground and ionospheric effects. On the other hand, a similar ANN model can extract the signal parameters from the final prediction dataset with an accuracy ranging from $97\%$ to $98\%$ when considering more realistic sets of the global 21cm signals based on physical models.

21 pages, 19 figures, Published in MNRAS

References in corpus (21)

• Cosmology at Low Frequencies: The 21 cm Transition and the High-Redshift Universe
• BIGHORNS - Broadband Instrument for Global HydrOgen ReioNisation Signal
• Power spectrum extraction for redshifted 21-cm epoch of reionization experiments: the LOFAR case
• Non-parametric foreground subtraction for 21cm epoch of reionization experiments
• Decoding the X-ray Properties of Pre-Reionization Era Sources
• Foreground Model and Antenna Calibration Errors in the Measurement of the Sky-Averaged λ21 cm Signal at z~20
• Emulation of reionization simulations for Bayesian inference of astrophysics parameters using neural networks
• maxsmooth: Rapid Maximally Smooth Function Fitting With Applications in Global 21-cm Cosmology
• GLOBALEMU: A novel and robust approach for emulating the sky-averaged 21-cm signal from the cosmic dawn and epoch of reionisation
• Ruling out 3 keV warm dark matter using 21 cm-EDGES data
• Quantifying Ionospheric Effects on Global 21-cm Observations
• Implicit Likelihood Inference of Reionization Parameters from the 21 cm Power Spectrum
• Improving constraints on the reionization parameters using 21-cm bispectrum
• 21cmVAE: A Very Accurate Emulator of the 21-cm Global Signal
• Global 21-cm signal extraction from foreground and instrumental effects IV: Accounting for realistic instrument uncertainties and their overlap with foreground and signal models
• Selection between foreground models for global 21-cm experiments
• Extracting the 21-cm Power Spectrum and the reionization parameters from mock datasets using Artificial Neural Networks
• Using Artificial Neural Networks to extract the 21-cm Global Signal from the EDGES data
• Bayesian Data Analysis for Sky-averaged 21-cm Experiments in the Presence of Ionospheric Effects
• 21cm Global Signal Extraction: Extracting the 21cm Global Signal using Artificial Neural Networks
• PINION: Physics-informed neural network for accelerating radiative transfer simulations for cosmic reionization

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• Uncertainty-Aware Neural Networks for Fuzzy Dark Matter Model Selection from \texorpdfstring{$x_{\mathrm{HI}}$}{x_HI} Measurements