Neural deprojection of galaxy stellar mass profiles

Conference item

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.

Authors: Yantovski-Barth, MJ; Zhang, H; Smyth, N; Stone, C; Bureau, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: NeurIPS
• Article number: 332
• Publication date: 2025-12-06
• Acceptance date: 2025-09-23
• Event title: Machine Learning and the Physical Sciences (ML4PS) Workshop at the 39th conference on Neural Information Processing Systems (NeurIPS 2025)
• Event location: San Diego, CA, USA
• Event website: https://ml4physicalsciences.github.io/2025/
• Event start date: 2025-12-06
• Event end date: 2025-12-06
• Language: English