Speaker
Description
Machine-learning approaches in astrophysics increasingly make use of high-dimensional generative models, for example in imaging, component separation, multi-instrument analysis, and simulation-based inference. These models enable flexible reconstructions and uncertainty quantification, but they also raise a central question: how can we decide whether additional model complexity is actually supported by the data?
I present a practical approach to Bayesian model selection based on estimating the Evidence Lower Bound (ELBO) within scalable variational inference schemes. The resulting estimate provides a computationally tractable proxy for the Bayesian evidence and can be used to compare competing astrophysical models, such as models with different physical components, instrumental descriptions, or prior assumptions. I discuss how posterior samples can be combined with analytic contributions to reduce estimator variance and make evidence estimation feasible in practice.
I will illustrate the method with applications to astrophysical imaging, component separation, and strong gravitational lensing, where principled model comparison is needed to distinguish genuine physical structure from noise fluctuations, instrumental effects, or model misspecification. This work connects uncertainty-aware machine learning, Bayesian evidence estimation, and information geometry, providing a route from generative reconstruction toward principled model criticism and selection in astronomy.