Accurate parameter inference for the Light-cone Epoch of Reionization 21-cm signal

The light-cone (LC) effect introduces line-of-sight (LoS) statistical inhomogeneity into the 21-cm signal. Consequently, the traditional power spectrum (PS) fails to capture the full two-point statistical information. The evolving power spectrum (ePS), P e(k, z), offers an alternative that accounts for this LoS evolution. We compare the statistical power of three different summary statistics: the standard cylindrical PS P(k,k), slice-wise PS P s(k, z) (3D PS for small bandwidth LC slices), and ePS P e(k, z). We first demonstrate that P e(k,z) successfully recovers the benchmark 3D PS of coeval simulations across most k and z, whereas the slice-wise PS recovers only at large k. To efficiently perform parameter inference, we train artificial neural network (ANN) emulators on 500 LC 21-cm signals. Our forecasts incorporate cosmic variance, estimated using 50 statistically independent realizations of the signal, alongside SKA-Low system noise for integration times of 1000 and 104 hrs. We find that ePS outperforms its peers, yielding 3 and 1.4 times tighter constraints than P(k,k) and P s(k,z), respectively. Our results establish the ePS as an optimal summary statistic for interpreting forthcoming data.