PyEMILI: A New Generation Computer-aided Spectral Line Identifier -- II. Emission-line Identification and Plasma Diagnostics of a Sample of Gaseous Nebulae

TL;DR

This paper tests and demonstrates the robustness of PyEMILI, a spectral-line identification tool, on emission-line nebulae, showing improved accuracy and providing plasma diagnostics through advanced modeling and MCMC analysis.

Contribution

It introduces enhancements to PyEMILI, including expanded atomic databases and new diagnostic capabilities for faint lines, establishing it as a reliable tool for nebular spectroscopy.

Findings

PyEMILI shows significant improvements in line identification accuracy.

The code can now generate theoretical spectra for plasma diagnostics.

MCMC analysis yields detailed physical parameters of nebulae.

Abstract

In order to test the robustness and reliability of the new generation spectral-line identifier PyEMILI, as initially introduced in Paper I, in line identification and establish a reference/benchmark dataset for future spectroscopic studies, we run the code on the line lists of a selected sample of emission-line nebulae, including planetary nebulae (PNe), HII regions, and Herbig-Haro (HH) objects with deep high-dispersion spectroscopic observations published over the past two decades. The automated line identifications by PyEMILI demonstrate significant improvements in both completeness and accuracy compared to the previous manual identifications in the literature. Since our last report of PyEMILI, the atomic transition database used by the code has been further expanded by cross-matching the Kurucz Line Lists. Moreover, to aid the PyEMILI identification of numerous faint optical recombination lines (ORLs) of CII, NII, OII and NeII, we compiled a new dataset of effective recombination coefficients for these nebular lines, and created a new subroutine in the code to generate theoretical spectra of heavy-element ORLs at various electron temperature and density cases; these theoretical spectra can be used to fit the observed recombination spectrum of a PN to obtain the electron temperature, density and ionic abundances using the Markov-Chain Monte Carlo (MCMC) method. We present MCMC-derived parameters for a sample of PNe. This work establishes PyEMILI as a robust and versatile tool for both line identification and plasma diagnostics in deep spectroscopy of gaseous nebulae.