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

X Zhao, Y Mao, S Zuo, BD Wandelt - arXiv preprint arXiv:2310.17602, 2023 - arxiv.org
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… 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 …

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BibTeX

@article{2310.17602v2,
Author        = {Xiaosheng Zhao and Yi Mao and Shifan Zuo and Benjamin D. Wandelt},
Title         = {Simulation-based Inference of Reionization Parameters from 3D
  Tomographic 21 cm Light-cone Images -- II: Application of Solid Harmonic
  Wavelet Scattering Transform},
Eprint        = {2310.17602v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.IM},
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.},
Year          = {2023},
Month         = {Oct},
Url           = {http://arxiv.org/abs/2310.17602v2},
File          = {2310.17602v2.pdf}
}

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