SDSS-IV MaNGA PyMorph Photometric and Deep Learning Morphological Catalogs and implications for bulge properties and stellar angular momentum. (arXiv:1811.02580v1 [astro-ph.GA])

SDSS-IV MaNGA PyMorph Photometric and Deep Learning Morphological Catalogs and implications for bulge properties and stellar angular momentum. (arXiv:1811.02580v1 [astro-ph.GA])
<a href="http://arxiv.org/find/astro-ph/1/au:+Fischer_J/0/1/0/all/0/1">J.-L. Fischer</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Sanchez_H/0/1/0/all/0/1">H. Dom&#xed;nguez S&#xe1;nchez</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Bernardi_M/0/1/0/all/0/1">M. Bernardi</a>

We describe the SDSS-IV MaNGA PyMorph Photometric (MPP-VAC) and MaNGA Deep
Learning Morphology (MDLM-VAC) Value Added Catalogs. The MPP-VAC provides
photometric parameters from S’ersic and S’ersic+Exponential fits to the 2D
surface brightness profiles of the MaNGA DR15 galaxy sample. Compared to
previous PyMorph analyses of SDSS imaging, our analysis of the MaNGA DR15
incorporates three improvements: the most recent SDSS images; modified criteria
for determining bulge-to-disk decompositions; and the fits in MPP-VAC have been
eye-balled, and re-fit if necessary, for additional reliability. A companion
catalog, the MDLM-VAC, provides Deep Learning-based morphological
classifications for the same galaxies. The MDLM-VAC includes a number of
morphological properties (e.g., a TType, and a finer separation between
elliptical and S0 galaxies). Combining the MPP- and MDLM-VACs allows to show
that the MDLM morphological classifications are more reliable than previous
work. It also shows that single-S’ersic fits to late- and early-type galaxies
are likely to return S’ersic indices of $n le 2$ and $ge 4$, respectively,
and this correlation between $n$ and morphology extends to the bulge component
as well. While the former is well-known, the latter contradicts some recent
work suggesting little correlation between $n$-bulge and morphology. Combining
both VACs with MaNGA’s spatially resolved spectroscopy allows us to study how
the stellar angular momentum depends on morphological type. We find
correlations between stellar kinematics, photometric properties, and
morphological type even though the spectroscopic data played no role in the
construction of the MPP- and MDLM-VACs.

We describe the SDSS-IV MaNGA PyMorph Photometric (MPP-VAC) and MaNGA Deep
Learning Morphology (MDLM-VAC) Value Added Catalogs. The MPP-VAC provides
photometric parameters from S’ersic and S’ersic+Exponential fits to the 2D
surface brightness profiles of the MaNGA DR15 galaxy sample. Compared to
previous PyMorph analyses of SDSS imaging, our analysis of the MaNGA DR15
incorporates three improvements: the most recent SDSS images; modified criteria
for determining bulge-to-disk decompositions; and the fits in MPP-VAC have been
eye-balled, and re-fit if necessary, for additional reliability. A companion
catalog, the MDLM-VAC, provides Deep Learning-based morphological
classifications for the same galaxies. The MDLM-VAC includes a number of
morphological properties (e.g., a TType, and a finer separation between
elliptical and S0 galaxies). Combining the MPP- and MDLM-VACs allows to show
that the MDLM morphological classifications are more reliable than previous
work. It also shows that single-S’ersic fits to late- and early-type galaxies
are likely to return S’ersic indices of $n le 2$ and $ge 4$, respectively,
and this correlation between $n$ and morphology extends to the bulge component
as well. While the former is well-known, the latter contradicts some recent
work suggesting little correlation between $n$-bulge and morphology. Combining
both VACs with MaNGA’s spatially resolved spectroscopy allows us to study how
the stellar angular momentum depends on morphological type. We find
correlations between stellar kinematics, photometric properties, and
morphological type even though the spectroscopic data played no role in the
construction of the MPP- and MDLM-VACs.

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