Simulation-based Inference of Reionization Parameters from 3D Tomographic 21 cm Light-cone Images -- II: Application of Solid Harmonic Wavelet Scattering Transform

TL;DR

This paper introduces the solid harmonic wavelet scattering transform as a fixed, non-trainable data compression method for 3D 21 cm images, improving Bayesian inference of reionization parameters over previous CNN-based approaches.

Contribution

It demonstrates that solid harmonic WST combined with DELFI outperforms CNN-based compression in parameter estimation from 21 cm images, including realistic observational effects.

Findings

WST-based analysis yields tighter credible regions than CNN-based methods.

WST outperforms 21 cm power spectrum analysis in parameter inference.

The approach is robust to noise and foreground residuals.

Abstract

The information regarding how the intergalactic medium is reionized by astrophysical sources is contained in the tomographic three-dimensional 21 cm images from the epoch of reionization. In Zhao et al. (2022a) ("Paper I"), we demonstrated for the first time that density estimation likelihood-free inference (DELFI) can be applied efficiently to perform a Bayesian inference of the reionization parameters from the 21 cm images. Nevertheless, the 3D image data needs to be compressed into informative summaries as the input of DELFI by, e.g., a trained 3D convolutional neural network (CNN) as in Paper I (DELFI-3D CNN). Here in this paper, we introduce an alternative data compressor, the solid harmonic wavelet scattering transform (WST), which has a similar, yet fixed (i.e. no training), architecture to CNN, but we show that this approach (i.e. solid harmonic WST with DELFI) outperforms earlier analyses based on 3D 21 cm images using DELFI-3D CNN in terms of credible regions of parameters. Realistic effects, including thermal noise and residual foreground after removal, are also applied to the mock observations from the Square Kilometre Array (SKA). We show that under the same inference strategy using DELFI, the 21 cm image analysis with solid harmonic WST outperforms the 21 cm power spectrum analysis. This research serves as a proof of concept, demonstrating the potential to harness the strengths of WST and simulation-based inference to derive insights from future 21 cm light-cone image data.