Speaker
Description
Deep generative models offer powerful tools for solving astrophysical inference problems by enabling flexible representations of prior knowledge and likelihood functions.
In the first part of the talk, I will discuss how generative models can be employed to construct likelihood functions for cosmological inference at the field level, enabling more effective extraction of information compared to traditional summary statistics like two-point statistics. This simulation-based inference framework facilitates anomaly detection of model misspecification, and enhances interpretability through sample generation. I will present applications to weak gravitational lensing analysis, particularly our ongoing work on applying this approach to the field-level analysis of the Hyper Suprime-Cam (HSC) survey.
In the second part, I will demonstrate how generative models can be used to construct physically informed priors for Bayesian inverse problems. As an application, I will show how this approach enables image reconstruction of AGN accretion disks from intensity interferometry, where only the amplitudes of Fourier modes are measured while phase information is lost. By sampling from the resulting posterior distribution, we achieve high-fidelity reconstructions with uncertainty quantification, outperforming traditional iterative methods across varying noise levels and UV-plane coverage.
| Session Selection | Astronomy and Astrophysics |
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