Machine learning astrophysics from 21 cm lightcones: impact of network
  architectures and signal contamination
  Imaging the cosmic 21 cm signal will map out the first billion years of our
Universe. The resulting 3D lightcone (LC) will encode the properties of the
unseen first galaxies and physical cosmology. Here, we build on previous work
using neural networks (NNs) to infer astrophysical parameters directly from 21
cm LC images. We introduce recurrent neural networks (RNNs), capable of
efficiently characterizing the evolution along the redshift axis of 21 cm LC
images. Using a large database of simulated cosmic 21 cm LCs, we compare the
relative performance in parameter estimation of different network
architectures. These including two types of RNNs, which differ in their
complexity, as well as a more traditional convolutional neural network (CNN).
For the ideal case of no instrumental effects, our simplest and easiest to
train RNN performs the best, with a mean squared parameter estimation error
(MSE) that is lower by a factor of $\ge 2$ compared with the other
architectures studied here, and a factor of $\ge 8$ lower than the
previously-studied CNN. We also corrupt the cosmic signal by adding noise
expected from a 1000 h integration with the Square Kilometre Array, as well as
excising a foreground-contaminated 'horizon wedge'. Parameter prediction errors
increase when the NNs are trained on these contaminated LC images, though
recovery is still good even in the most pessimistic case (with $R^2 \ge
0.5-0.95$). However, we find no notable differences in performance between
network architectures on the contaminated images. We argue this is due to the
size of our data set, highlighting the need for larger data sets and/or better
data augmentation in order to maximize the potential of NNs in 21 cm parameter
estimation.
