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Creating a database of 21cm brightness temperature signals from the Epoch ofReionisation (EoR) for an array of reionisation histories is a complex andcomputationally expensive task, given the range of astrophysical processesinvolved and the possibly high-dimensional parameter space that is to beprobed. We utilise a specific type of neural network, a Progressively GrowingGenerative Adversarial Network (PGGAN), to produce realistic tomography imagesof the 21cm brightness temperature during the EoR, covering a continuousthree-dimensional parameter space that models varying X-ray emissivity, Lymanband emissivity, and ratio between hard and soft X-rays. The GPU-trainednetwork generates new samples at a resolution of $\sim 3'$ in a second (on alaptop CPU), and the resulting global 21cm signal, power spectrum, and pixeldistribution function agree well with those of the training data, taken fromthe 21SSD catalogue \citep{Semelin2017}. Finally, we showcase how a trainedPGGAN can be leveraged for the converse task of inferring parameters from 21cmtomography samples via Approximate Bayesian Computation.

A unified framework for 21cm tomography sample generation and parameter inference with Progressively Growing GANs