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

## TL;DR

This paper demonstrates that deep learning, specifically CNNs, can accurately extract key astrophysical and cosmological parameters from simulated 21cm maps, even with realistic SKA noise, aiding future cosmological studies.

## Contribution

The authors develop CNN architectures capable of simultaneously estimating multiple astrophysical and cosmological parameters from 21cm maps, including realistic SKA-like noise conditions.

## Key findings

- CNNs recover parameters with over 92% accuracy under noise
- Accuracy improves to over 99% at low redshift and neutral fraction
- Future 21cm observations can tightly constrain parameters with few frequency channels

## Abstract

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.