Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userConstraining the reionization history using deep learning from 21cm tomography with the Square Kilometre Array
167
0
0.0
(
0
)
Authors
Tumelo Mangena
Ask ChatGPT about the research
No Arabic abstract
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}.
rate research
Read More
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 neural networks (CNN), to simultaneously estimate the astrophysical and cosmological parameters from 21cm maps from semi-numerical simulations. We further convert the simulated 21cm maps into SKA-like mock maps using the detailed SKA antennae distribution, thermal noise and a recipe for foreground cleaning. We successfully design two CNN architectures (VGGNet-like and ResNet-like) that are both efficiently able to extract simultaneously three astrophysical parameters, namely the photon escape fraction (f$_{rm esc}$), the ionizing emissivity power dependence on halo mass ($C_{rm ion}$) and the ionizing emissivity redshift evolution index ($D_{rm ion}$), and three cosmological parameters, namely the matter density parameter ($Omega_{m}$), the dimensionless Hubble constant ($h$), and the matter fluctuation amplitude ($sigma_{8}$), from 21cm maps at several redshifts. With the presence of noise from SKA, our designed CNNs are still able to recover these astrophysical and cosmological parameters with great accuracy ($R^{2} > 92%$), improving to $R^{2} > 99%$ towards low redshift and low neutral fraction values. Our results show that future 21cm observations can play a key role to break degeneracy between models and tightly constrain the astrophysical and cosmological parameters, using only few frequency channels.
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 contribution 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.
The Square Kilometre Array (SKA) will have a low frequency component (SKA-low) which has as one of its main science goals the study of the redshifted 21cm line from the earliest phases of star and galaxy formation in the Universe. This 21cm signal provides a new and unique window on both the formation of the first stars and accreting black holes and the later period of substantial ionization of the intergalactic medium. The signal will teach us fundamental new things about the earliest phases of structure formation, cosmology and even has the potential to lead to the discovery of new physical phenomena. Here we present a white paper with an overview of the science questions that SKA-low can address, how we plan to tackle these questions and what this implies for the basic design of the telescope.
We quantitatively investigate the possibility of detecting baryonic acoustic oscillations (BAO) using single-dish 21cm intensity mapping observations in the post-reionization era. We show that the telescope beam smears out the isotropic BAO signature and, in the case of the Square Kilometer Array (SKA) instrument, makes it undetectable at redshifts $zgtrsim1$. We however demonstrate that the BAO peak can still be detected in the radial 21cm power spectrum and describe a method to make this type of measurements. By means of numerical simulations, containing the 21cm cosmological signal as well as the most relevant Galactic and extra-Galactic foregrounds and basic instrumental effect, we quantify the precision with which the radial BAO scale can be measured in the 21cm power spectrum. We systematically investigate the signal-to-noise and the precision of the recovered BAO signal as a function of cosmic variance, instrumental noise, angular resolution and foreground contamination. We find that the expected noise levels of SKA would degrade the final BAO errors by $sim5%$ with respect to the cosmic-variance limited case at low redshifts, but that the effect grows up to $sim65%$ at $zsim2-3$. Furthermore, we find that the radial BAO signature is robust against foreground systematics, and that the main effect is an increase of $sim20%$ in the final uncertainty on the standard ruler caused by the contribution of foreground residuals as well as the reduction in sky area needed to avoid high-foreground regions. We also find that it should be possible to detect the radial BAO signature with high significance in the full redshift range. We conclude that a 21cm experiment carried out by the SKA should be able to make direct measurements of the expansion rate $H(z)$ with competitive per-cent level precision on redshifts $zlesssim2.5$.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
