Vivisecting galaxies with BANG: an automated morpho-kinematical decomposition of the SDSS-DR17 MaNGA survey

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 framework oversimplifies complexity, especially if one considers galaxy...

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Veröffentlicht in:arXiv.org 2023-05
Hauptverfasser: Rigamonti, Fabio, Dotti, Massimo, Covino, Stefano, Haardt, Francesco, Cortese, Luca, Landoni, Marco, Varisco, Ludovica
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Sprache:eng
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Zusammenfassung: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 framework oversimplifies complexity, especially if one considers galaxy kinematic.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 kinematical properties of galaxies. Here we apply \textsc{bang} to the SDSS-MaNGA survey, estimating key parameters such as mass, radial extensions, 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 agreement in the recovered total stellar mass. We also manage to reproduce well-established scaling relations, demonstrating how a proper dynamical modelling can result in tighter correlations and provide corrections to standard approaches. Finally, we propose a more general way of decomposing galaxies into "hot" and "cold" components, showing a correlation with orbit-based approaches and visually determined morphological type. Unexpected tails in the "hot-to-total" mass-ratio distribution are present for galaxies of all morphologies, possibly due to visual morphology misclassifications.
ISSN:2331-8422
DOI:10.48550/arxiv.2305.03762