Atomic Data Assessment with PyNeb
Christophe Morisset (1), Valentina Luridiana (2,3), Jorge, Garc\'ia-Rojas (2,3), Ver\'onica G\'omez-Llanos (1), Manuel A. Bautista (4), and Claudio Mendoza (4,5) ((1) Inst\'ituto de Astronom\'ia, UNAM, M\'exico, (2) Instituto de Astrof\'isica de Canarias, La Laguna, Tenerife

TL;DR
This paper uses the PyNeb Python package to evaluate and improve atomic data accuracy for nebular diagnostics, significantly reducing uncertainties in emission line modeling and assessing new collision strength datasets.
Contribution
It provides a practical framework for atomic data assessment using PyNeb, critically evaluates the accuracy of atomic datasets, and improves the reliability of nebular diagnostics.
Findings
Emissivity-ratio uncertainties reduced to within 10%.
Collision strength data for [N II] and [O III] do not significantly affect temperature diagnostics.
Benchmarking shows scatter in collision data does not exceed 10% in diagnostics.
Abstract
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…
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