Atomic Data Assessment with PyNeb
  PyNeb is a Python package widely used to model emission lines in gaseous
nebulae. We take advantage of its object-oriented architecture, class methods,
and historical atomic database to structure a practical environment for atomic
data assessment. Our aim is to reduce the uncertainties in parameter space
(line-ratio diagnostics, electron density and temperature, and ionic
abundances) arising from the underlying atomic data by critically selecting the
PyNeb default datasets. We evaluate the questioned radiative-rate accuracy of
the collisionally excited forbidden lines of the N- and P-like ions (O II, Ne
IV, S II, Cl III, and Ar IV), which are used as density diagnostics. With the
aid of observed line ratios in the dense NGC 7027 planetary nebula and careful
data analysis, we arrive at emissivity-ratio uncertainties from the radiative
rates within 10\%, a considerable improvement over a previously predicted 50\%.
We also examine the accuracy of an extensive dataset of electron-impact
effective collision strengths for the carbon isoelectronic sequence recently
published. By estimating the impact of the new data on the pivotal temperature
diagnostics of [N II] and [O III] and by benchmarking the collision strength
with a measured resonance position, we question their usefulness in nebular
modeling. We confirm that the effective-collision-strength scatter of selected
datasets for these two ions does not lead to uncertainties in the temperature
diagnostics larger than 10\%.
