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Register a new userGLOBALEMU: A novel and robust approach for emulating the sky-averaged 21-cm signal from the cosmic dawn and epoch of reionisation
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Emulation of the Global (sky-averaged) 21-cm signal from the Cosmic Dawn and Epoch of Reionization with neural networks has been shown to be an essential tool for physical signal modelling. In this paper we present globalemu, a Global 21-cm signal emulator that uses redshift as a character defining variable along side a set of astrophysical parameters to estimate the brightness temperature of the 21-cm signal. Combined with a physically motivated pre-processing of the data this makes for a reliable and fast emulator that is relatively insensitive to the neural network design. A single high resolution signal can be emulated in 1.3 ms when using globalemu in comparison to 133 ms, a factor of 102 improvement, when using the existing public state of the art emulator 21cmGEM evaluated with the same computing power. We illustrate, with the same training and test data used for 21cmGEM, that globalemu is almost twice as accurate as 21cmGEM and for 95% of models in a test set of $approx1,700$ we can achieve a RMSE of $leq 5.37$ mK and a mean RMSE of 2.52 mK across the band z = 7 -28 (approximately 10% the expected noise of 25 mK for the Radio Experiment for the Analysis of Cosmic Hydrogen (REACH)). Further, globalemu provides a flexible framework in which the neutral fraction history and Global signal models with updated astrophysics can be emulated easily. The emulator is pip installable and available at: https://github.com/htjb/globalemu. globalemu will be used by the REACH collaboration to perform physical signal modelling inside a Bayesian nested sampling loop.
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The 21-cm signal of neutral hydrogen is a sensitive probe of the Epoch of Reionization (EoR) and Cosmic Dawn. Currently operating radio telescopes have ushered in a data-driven era of 21-cm cosmology, providing the first constraints on the astrophysical properties of sources that drive this signal. However, extracting astrophysical information from the data is highly non-trivial and requires the rapid generation of theoretical templates over a wide range of astrophysical parameters. To this end emulators are often employed, with previous efforts focused on predicting the power spectrum. In this work we introduce 21cmGEM - the first emulator of the global 21-cm signal from Cosmic Dawn and the EoR. The smoothness of the output signal is guaranteed by design. We train neural networks to predict the cosmological signal using a database of ~30,000 simulated signals which were created by varying seven astrophysical parameters: the star formation efficiency and the minimal mass of star-forming halos; the efficiency of the first X-ray sources and their spectrum parameterized by spectral index and the low energy cutoff; the mean free path of ionizing photons and the CMB optical depth. We test the performance with a set of ~2,000 simulated signals, showing that the relative error in the prediction has an r.m.s. of 0.0159. The algorithm is efficient, with a running time per parameter set of 0.16 sec. Finally, we use the database of models to check the robustness of relations between the features of the global signal and the astrophysical parameters that we previously reported.
We explore methods for robust estimation of the 21 cm signal from the Epoch of Reionisation (EoR). A Kernel Density Estimator (KDE) is introduced for measuring the spatial temperature fluctuation power spectrum from the EoR. The KDE estimates the underlying probability distribution function of fluctuations as a function of spatial scale, and contains different systematic biases and errors to the typical approach to estimating the fluctuation power spectrum. Extraction of histograms of visibilities allows moments analysis to be used to discriminate foregrounds from 21 cm signal and thermal noise. We use the information available in the histograms, along with the statistical dis-similarity of foregrounds from two independent observing fields, to robustly separate foregrounds from cosmological signal, while making no assumptions about the Gaussianity of the signal. Using two independent observing fields to robustly discriminate signal from foregrounds is crucial for the analysis presented in this paper. We apply the techniques to 13 hours of Murchison Widefield Array (MWA) EoR data over two observing fields. We compare the output to that obtained with a comparative power spectrum estimation method, and demonstrate the reduced foreground contamination using this approach. Using the second moment obtained directly from the KDE distribution functions yields a factor of 2-3 improvement in power for k < 0.3hMpc^{-1} compared with a matched delay space power estimator, while weighting data by additional statistics does not offer significant improvement beyond that available for thermal noise-only weights.
Studying the cosmic dawn and the epoch of reionization through the redshifted 21 cm line are among the major science goals of the SKA1. Their significance lies in the fact that they are closely related to the very first stars in the universe. Interpreting the upcoming data would require detailed modelling of the relevant physical processes. In this article, we focus on the theoretical models of reionization that have been worked out by various groups working in India with the upcoming SKA in mind. These models include purely analytical and semi-numerical calculations as well as fully numerical radiative transfer simulations. The predictions of the 21 cm signal from these models would be useful in constraining the properties of the early galaxies using the SKA data.
Efforts are being made to observe the 21-cm signal from the cosmic dawn using sky-averaged observations with individual radio dipoles. In this paper, we develop a model of the observations accounting for the 21-cm signal, foregrounds, and several major instrumental effects. Given this model, we apply Markov Chain Monte Carlo techniques to demonstrate the ability of these instruments to separate the 21-cm signal from foregrounds and quantify their ability to constrain properties of the first galaxies. For concreteness, we investigate observations between 40 and 120 MHz with the proposed DARE mission in lunar orbit, showing its potential for science return.
The 21-cm signal from the Cosmic Dawn (CD) is likely to contain large fluctuations, with the most extreme astrophysical models on the verge of being ruled out by observations from radio interferometers. It is therefore vital that we understand not only the astrophysical processes governing this signal, but also other inherent processes impacting the signal itself, and in particular line-of-sight effects. Using our suite of fully numerical radiative transfer simulations, we investigate the impact on the redshifted 21-cm from the CD from one of these processes, namely the redshift-space distortions (RSDs). When RSDs are added, the resulting boost to the power spectra makes the signal more detectable for our models at all redshifts, further strengthening hopes that a power spectra measurement of the CD will be possible. RSDs lead to anisotropy in the signal at the beginning and end of the CD, but not while X-ray heating is underway. The inclusion of RSDs, however, decreases detectability of the non-Gaussianity of fluctuations from inhomogeneous X-ray heating measured by the skewness and kurtosis. On the other hand, mock observations created from all our simulations that include telescope noise corresponding to 1000 h observation with the Square Kilometre Array telescope show that we may be able image the CD for all heating models considered and suggest RSDs dramatically boost fluctuations coming from the inhomogeneous Ly-$alpha$ background.
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