Decomposing galaxies with BANG: an automated morpho-kinematical decomposition of the SDSS-DR17 MaNGA survey
Fabio Rigamonti, Massimo Dotti, Stefano Covino, Francesco Haardt, Luca, Cortese, Marco Landoni, Ludovica Varisco

TL;DR
This paper introduces BANG, a fast and reliable automated method for decomposing galaxy morpho-kinematics in large surveys, improving understanding of galaxy structure and dynamics.
Contribution
The paper presents BANG, a novel GPU-based analytical approach for galaxy decomposition that integrates morphological and kinematic data, applied to the SDSS-MaNGA survey.
Findings
BANG accurately estimates galaxy parameters for over 10,000 objects.
Results agree with orbit-based algorithms in total stellar mass recovery.
Proper dynamical modelling yields tighter scaling relations.
Abstract
From a purely photometric perspective galaxies are generally decomposed into a bulge+disc system, with bulges being dispersion-dominated and discs rotationally-supported. However, recent observations have demonstrated that such a framework oversimplifies complexity, especially if one considers galaxy kinematics. To address this issue we introduced with the GPU-based code \textsc{bang} a novel approach that employs analytical potential-density pairs as galactic components, allowing for a computationally fast, still reliable fit of the morphological and kinematic properties of galaxies. Here we apply \textsc{bang} to the SDSS-MaNGA survey, estimating key parameters such as mass, radial extensions, and dynamics, for both bulges and discs of +10,000 objects. We test our methodology against a smaller subsample of galaxies independently analysed with an orbit-based algorithm, finding…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsData Visualization and Analytics · Time Series Analysis and Forecasting · Remote Sensing in Agriculture
