Neural Deprojection of Galaxy Stellar Mass Profiles

TL;DR

This paper presents a neural network-based method for deprojecting galaxy stellar mass profiles, enabling more flexible and dust-robust dynamical modeling of galaxies for black hole mass inference.

Contribution

It introduces a neural approach that replaces traditional imaging deprojection with a differentiable model integrated into a dynamical pipeline, extending analysis to dust-obscured galaxies.

Findings

Results consistent with state-of-the-art models.

Applicable to dust-obscured and active galaxies.

Integrated into a Bayesian inference pipeline.

Abstract

We introduce a neural approach to dynamical modeling of galaxies that replaces traditional imaging-based deprojections with a differentiable mapping. Specifically, we train a neural network to translate Nuker profile parameters into analytically deprojectable Multi Gaussian Expansion components, enabling physically realistic stellar mass models without requiring optical observations. We integrate this model into SuperMAGE, a differentiable dynamical modelling pipeline for Bayesian inference of supermassive black hole masses. Applied to ALMA data, our approach finds results consistent with state-of-the-art models while extending applicability to dust-obscured and active galaxies where optical data analysis is challenging.