We apply a variety of statistical summaries of the 21cm signal for EoR analyses.  In previous work, we use the 21cm power spectrum (PS) to distinguish inside-out and outside-in morphologies of reionization with mock observations using Bayesian model selection. 

We expand our previous work with a model that includes X-ray heating, we look in detail at how many redshifts an observation must span to decisively distinguish a saturated from non-saturated spin temperature. We also include UV luminosity functions to gain synergy with HST and JWST observations. Recently, we distinguished EoR morphologies by integrating learnt-posterior distributions with pyDelfi when summarising the light-cone with a 3D-CNN. ‘Likelihood-free` inference provides greater precision than the 21cm PS and can distinguish morphologies decisively, but it is not as flexible as the PS when it comes to successful parameter estimation on different models. 

Lastly, we replace the standard Fourier transform within the PS with the Morlet wavelet transform to construct the Morlet PS, a statistic that is ergodic of the entire light-cone. Our current work shows a significant precision increase in parameter estimation when compared to the PS because we evolve wavelets along the line-of-sight to remove bias from the light-cone effect. However, as the statistic evolves, the Bayesian likelihood must include a covariance term which currently picks up simulation artefacts along the line-of-sight caused by wrapping coeval cubes throughout the light-cone length. We are developing a version of 21cmFAST that contains structure modes that span the line-of-sight length of the light-cone to remedy this.