This catalog provides photometric parameters obtained from Sersic and Sersic+Exponential fits to the 2D surface brightness profiles of the MaNGA DR15 galaxy sample. We use the PyMorph algorithm for determining the fits. PyMorph has been extensively tested (Meert et al. 2013; Fischer et al. 2017; Bernardi et al. 2017), and PyMorph reductions of SDSS DR7 galaxies are available (the UPenn SDSS PhotDec Catalog: Meert et al. 2015; Meert et al. 2016). Since about 20% of the MaNGA DR15 galaxies were not included in that analysis, we have re-run PyMorph for all the galaxies in the MaNGA DR15 sample. These re-runs incorporate three improvements: they use the SDSS DR14 images, improved bulge-to-disk decomposition by slightly modifying our criteria when using PyMorph, and all the fits in this catalog have been eye-balled, and re-fit if necessary, for additional reliability (Fischer et al. 2018). We recommend using ``flag_fit'', as explained in the data model.
A companion catalog (MaNGA Morphology Deep Learning) provides morphological classifications based on Deep Learning (DL) for the same set of galaxies. The methodology for training and testing the DL models is described in detail in Domínguez Sánchez et al. (2018), where classifications for the 670,000 objects from the SDSS DR7 Main Galaxy Sample of Meert et al. (2015) are provided.
Note that all position angles here are with respect to the camera columns in the SDSS ``fpC'' images (which are not aligned with the North direction); to convert to the convention where North is up, East is left (note that the MaNGA datacubes have North up, East right) set PA_MaNGA = (90-PA_PyMorph) - SPA, where PA_PyMorph is the value given in this catalogue, and SPA is the SDSS camera column position angle with respect to North reported in the primary header of the ``fpC'' SDSS images. PA_MaNGA is defined to increase from East towards North. The SPA angles for this catalog are provided in a separate file which can be downloaded here.