3D ScatterNet: Inference from 21 cm Light-cones
arXiv:2307.09530
Abstract
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.
arXiv comment9 pages, 4 figures, 2 tables. Accepted to ICML 2023 Machine Learning for Astrophysics workshop. Comments and suggestions are welcome
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