SciPost Submission Page
Optimal, fast, and robust inference of reionization-era cosmology with the 21cmPIE-INN
by Benedikt Schosser, Caroline Heneka, Tilman Plehn
Submission summary
| Authors (as registered SciPost users): | Tilman Plehn · Benedikt Schosser |
| Submission information | |
|---|---|
| Preprint Link: | https://arxiv.org/abs/2401.04174v2 (pdf) |
| Date submitted: | 2025-01-17 10:07 |
| Submitted by: | Schosser, Benedikt |
| Submitted to: | SciPost Physics Core |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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| Approaches: | Computational, Phenomenological |
Abstract
Modern machine learning will allow for simulation-based inference from reionization-era 21cm observations at the Square Kilometre Array. Our framework combines a convolutional summary network and a conditional invertible network through a physics-inspired latent representation. It allows for an optimal and extremely fast determination of the posteriors of astrophysical and cosmological parameters. The sensitivity to non-Gaussian information makes our method a promising alternative to the established power spectra.
List of changes
Introduction:
Highlight more how our approach differs from previous ones (page 2)
Section 2:
2.1
- Fix typo (page 4)
2.2
-Rename section to Neural Posterior Estimation (page 4)
-Remove most mentions of BayesFlow (whole paper)
2.3
-Discuss higher dimensional latent space (page 7&8)
2.4
- Give more information on training scheme (page 8)
- More context on how the summary is adjusted (page 9)
2.5
- Discuss SBC much more (page 10)
Section 3:
3.2
Point out that SBC is not possible with MCMC (page 14)