Constraining the astrophysics and cosmology from 21cm tomography using deep learning with the SKA

Understand

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.