  The Square Kilometre Array (SKA) will have the sensitivity to take the 3D
light-cones of the 21 cm signal from the epoch of reionization. This signal,
however, is highly non-Gaussian and can not be fully interpreted by the
traditional statistic using power spectrum. In this work, we introduce the 3D
ScatterNet that combines the normalizing flows with solid harmonic wavelet
scattering transform, a 3D CNN featurizer with inductive bias, to perform
implicit likelihood inference (ILI) from 21 cm light-cones. We show that 3D
ScatterNet outperforms the ILI with a fine-tuned 3D CNN in the literature. It
also reaches better performance than ILI with the power spectrum for varied
light-cone effects and varied signal contaminations.
