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Register a new userDetection of Signals from Cosmic Reionization using Radio Interferometric Signal Processing
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A. Datta
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Observations of the HI 21cm transition line promises to be an important probe into the cosmic dark ages and epoch of reionization. One of the challenges for the detection of this signal is the accuracy of the foreground source removal. This paper investigates the extragalactic point source contamination and how accurately the bright sources ($gtrsim 1$ ~Jy) should be removed in order to reach the desired RMS noise and be able to detect the 21cm transition line. Here, we consider position and flux errors in the global sky-model for these bright sources as well as the frequency independent residual calibration errors. The synthesized beam is the only frequency dependent term included here. This work determines the level of accuracy for the calibration and source removal schemes and puts forward constraints for the design of the cosmic reionization data reduction scheme for the upcoming low frequency arrays like MWA,PAPER, etc. We show that in order to detect the reionization signal the bright sources need to be removed from the data-sets with a positional accuracy of $sim 0.1$ arc-second. Our results also demonstrate that the efficient foreground source removal strategies can only tolerate a frequency independent antenna based mean residual calibration error of $lesssim 0.2 %$ in amplitude or $lesssim 0.2$ degree in phase, if they are constant over each days of observations (6 hours). In future papers we will extend this analysis to the power spectral domain and also include the frequency dependent calibration errors and direction dependent errors (ionosphere, primary beam, etc).
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Characterizing the epoch of reionization (EoR) at $zgtrsim 6$ via the redshifted 21 cm line of neutral Hydrogen (HI) is critical to modern astrophysics and cosmology, and thus a key science goal of many current and planned low-frequency radio telescopes. The primary challenge to detecting this signal is the overwhelmingly bright foreground emission at these frequencies, placing stringent requirements on the knowledge of the instruments and inaccuracies in analyses. Results from these experiments have largely been limited not by thermal sensitivity but by systematics, particularly caused by the inability to calibrate the instrument to high accuracy. The interferometric bispectrum phase is immune to antenna-based calibration and errors therein, and presents an independent alternative to detect the EoR HI fluctuations while largely avoiding calibration systematics. Here, we provide a demonstration of this technique on a subset of data from the Hydrogen Epoch of Reionization Array (HERA) to place approximate constraints on the brightness temperature of the intergalactic medium (IGM). From this limited data, at $z=7.7$ we infer $1sigma$ upper limits on the IGM brightness temperature to be $le 316$ pseudo mK at $kappa_parallel=0.33$ pseudo $h$ Mpc$^{-1}$ (data-limited) and $le 1000$ pseudo mK at $kappa_parallel=0.875$ pseudo $h$ Mpc$^{-1}$ (noise-limited). The pseudo units denote only an approximate and not an exact correspondence to the actual distance scales and brightness temperatures. By propagating models in parallel to the data analysis, we confirm that the dynamic range required to separate the cosmic HI signal from the foregrounds is similar to that in standard approaches, and the power spectrum of the bispectrum phase is still data-limited (at $gtrsim 10^6$ dynamic range) indicating scope for further improvement in sensitivity as the array build-out continues.
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.
The epoch of reionization is one of the least known chapters in the evolutionary history of the Universe. This thesis investigates two major approaches to unveil the reionization history of the Universe using HI 21-cm maps.The most discussed approach has been to study the global statistical properties of the reionization HI 21-cm. We develop the formalism to calculate the Multi-frequency Angular Power Spectrum (MAPS) and quantify the statistics of the HI signal as a joint function of the angular multipole l and frequency separation Delta u. We adopt a simple model for the HI distribution which incorporates patchy reionization and use it to study the signatures of ionized bubbles on MAPS. We also study the implications of the foreground subtraction. A major part of the thesis investigates the possibility of detecting ionized bubbles around individual sources in 21-cm maps. We present a visibility based matched filter technique to optimally combine the signal from an ionized bubble and minimize the noise and foreground contributions. The formalism makes definite predictions on the ability to detect an ionized bubble or conclusively rule out its presence within a radio map. Results are presented for the GMRT and the MWA. Using simulated HI maps we analyzed the impact of HI fluctuations outside the bubble on its detectability. Various other issues such as (i) bubble size determination (ii) blind search for bubbles, (iii) optimum redshift for bubble detection are also discussed.
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.
We propose an algorithm for the reconstruction of the signal induced by cosmic strings in the cosmic microwave background (CMB), from radio-interferometric data at arcminute resolution. Radio interferometry provides incomplete and noisy Fourier measurements of the string signal, which exhibits sparse or compressible magnitude of the gradient due to the Kaiser-Stebbins (KS) effect. In this context the versatile framework of compressed sensing naturally applies for solving the corresponding inverse problem. Our algorithm notably takes advantage of a model of the prior statistical distribution of the signal fitted on the basis of realistic simulations. Enhanced performance relative to the standard CLEAN algorithm is demonstrated by simulated observations under noise conditions including primary and secondary CMB anisotropies.
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