Neural Deprojection of Galaxy Stellar Mass Profiles
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| author | Yantovski-Barth, M. J. Zhang, Hengyue Smyth, Nolan Stone, Connor Bureau, Martin Hezaveh, Yashar Perreault-Levasseur, Laurence |
| author_facet | Yantovski-Barth, M. J. Zhang, Hengyue Smyth, Nolan Stone, Connor Bureau, Martin Hezaveh, Yashar Perreault-Levasseur, Laurence |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_20746 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Deprojection of Galaxy Stellar Mass Profiles Yantovski-Barth, M. J. Zhang, Hengyue Smyth, Nolan Stone, Connor Bureau, Martin Hezaveh, Yashar Perreault-Levasseur, Laurence Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics 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. |
| title | Neural Deprojection of Galaxy Stellar Mass Profiles |
| topic | Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2511.20746 |