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تسجيل مستخدم جديدConstraining the reionization history using deep learning from 21cm tomography with the Square Kilometre Array
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Tumelo Mangena
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Upcoming 21cm surveys with the SKA1-LOW telescope will enable imaging of the neutral hydrogen distribution on cosmological scales in the early Universe. These surveys are expected to generate huge imaging datasets that will encode more information than the power spectrum. This provides an alternative unique way to constrain the reionization history, which might break the degeneracy in the power spectral analysis. Using Convolutional Neural Networks (CNN), we create a fast estimator of the neutral fraction from the 21cm maps that are produced by our large semi-numerical simulation. Our estimator is able to efficiently recover the neutral fraction ($x_{rm HI}$) at several redshifts with a high accuracy of 99% as quantified by the coefficient of determination $R^{2}$. Adding the instrumental effects from the SKA design slightly increases the loss function, but nevertheless we are still able to recover the neutral fraction with a similar high accuracy of 98%, which is only 1 per cent less. While a weak dependence on redshift is observed, the accuracy increases rapidly with decreasing neutral fraction. This is due to the fact that the instrumental noise increases towards high redshift where the Universe is highly neutral. Our results show the promise of directly using 21cm-tomography to constrain the reionization history in a model independent way, complementing similar efforts, such as those of the optical depth measurements from the Cosmic Microwave Background (CMB) observations by {it Planck}.
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Future Square Kilometre Array (SKA) surveys are expected to generate huge datasets of 21cm maps on cosmological scales from the Epoch of Reionization (EoR). We assess the viability of exploiting machine learning techniques, namely, convolutional neur
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Detections of the cross correlation signal between the 21cm signal during reionization and high-redshift Lyman Alpha emitters (LAEs) are subject to observational uncertainties which mainly include systematics associated with radio interferometers and
LAE selection. These uncertainties can be reduced by increasing the survey volume and/or the survey luminosity limit, i.e. the faintest detectable Lyman Alpha (Ly$alpha$) luminosity. We use our model of high-redshift LAEs and the underlying reionization state to compute the uncertainties of the 21cm-LAE cross correlation function at $zsimeq6.6$ for observations with SKA1-Low and LAE surveys with $Delta z=0.1$ for three different values of the average IGM ionization state ($langlechi_mathrm{HI}rangle$=0.1, 0.25, 0.5). At $zsimeq6.6$, we find SILVERRUSH type surveys, with a field of view of 21 deg$^2$ and survey luminosity limits of $L_alphageq7.9times10^{42}$erg~s$^{-1}$, to be optimal to distinguish between an inter-galactic medium (IGM) that is 50%, 25% and 10% neutral, while surveys with smaller fields of view and lower survey luminosity limits, such as the 5 and 10 deg$^2$ surveys with WFIRST, can only discriminate between a 50% and 10% neutral IGM.
Next-generation 21cm observations will enable imaging of reionization on very large scales. These images will contain more astrophysical and cosmological information than the power spectrum, and hence providing an alternative way to constrain the con
tribution of different reionizing sources populations to cosmic reionization. Using Convolutional Neural Networks, we present a simple network architecture that is sufficient to discriminate between Galaxy-dominated versus AGN-dominated models, even in the presence of simulated noise from different experiments such as the HERA and SKA.
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