Neural Posterior Estimation for X-ray reflection spectroscopy: Training on complex physical models and AGN Observation-Driven Parameter Grids.
Description
The development of an automated inference tool tailored to extract key physical parameters from obscured AGN (active galactic nuclei) X-ray spectra by means of more complex physical models than ever before with machine learning. For our pilot work, we use the decoupled X-ray reflection MYTorus model with separate direct and scattered ("reflected") continua, as well as narrow Fe K fluorescense.
Detailed example
This approach demonstrates a path to likelihood-free posterior estimation using neural networks, providing a scalable alternative to traditional methods for parameter inference in complex astrophysical models.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Such a complex model poses a significant computational challenge for traditional inference techniques. To address this, we construct a physically informed, observation-driven training grid, based on the parameter space spanned by nearby AGN observed with NuSTAR. We use this grid to train a Neural Posterior Estimation (NPE) model within a simulation-based inference (SBI) context. The parameters inferred are the photon index (Γ), the global and line-of-sight equivalent neutral hydrogen column densities (N_Hs and N_Hz), and the reflection scaling factor (A_S), each with associated uncertainties.
Controls / human review
ATO: Not reported; PIA: Not published