Xihan Ji

TL;DR: The data analysis pipeline (DAP) as discussed by the authors provides higher-level data products, such as stellar kinematics (velocity and velocity dispersion), emission-line properties (kinematics, fluxes, and equivalent widths), and spectral indices (e.g., D4000 and the Lick indices).

TL;DR: In this article, the reliability of the emission-line fluxes and kinematic properties derived by the MaNGA Data Analysis Pipeline (DAP) is analyzed and the effect of the algorithmic choices made in the DAP with regards to measuring emissionline properties, and the effects of simultaneously fitting the continuum and line emission.

TL;DR: In this article, the reliability of the emission-line fluxes and kinematic properties derived by the MaNGA Data Analysis Pipeline (DAP) is studied and the effects of random errors are quantified by studying various fit-quality metrics, idealized recovery simulations, and repeat observations.

TL;DR: The data analysis pipeline (DAP) as discussed by the authors provides higher-level data products, such as stellar kinematics (velocity and velocity dispersion), emission-line properties (kinematics, fluxes, and equivalent widths), and spectral indices (e.g., D4000 and the Lick indices).

TL;DR: The authors used the statistical power of the MaNGA integral field spectroscopic galaxy survey to improve the definition of strong line diagnostic boundaries used to classify gas ionization properties in galaxies.