Speaker
Description
We investigate whether matter-induced deviations from a vacuum black hole spacetime can be constrained through redshift observations of orbiting stars. Building on our previous study of redshift signatures in fluid-perturbed black hole backgrounds, we consider the problem of efficiently connecting model parameters to observable redshift curves in a setting relevant for data analysis. Since repeated evaluations of the full relativistic model can be computationally expensive in inference workflows, we propose a machine-learning surrogate for the redshift signal. The surrogate is trained on synthetic datasets generated from the underlying general-relativistic model and is designed to reproduce the dependence of the redshift curve on the spacetime's physical parameters and the orbit's parameters. This framework is intended as a first step toward comparisons with real observational data, allowing for fast exploration of parameter space and future parameter-estimation studies. We discuss the construction of the training set, the calibration of the redshift observable, and the accuracy requirements needed for the surrogate to remain physically reliable. Our results aim to provide a practical bridge between theoretical models of non-vacuum black hole environments and data-driven tests based on spectroscopic observations.