Galaxy emission line classification using 3D line ratio diagrams

Abstract

Two-dimensional (2D) line ratio diagnostic diagrams have become a key tool in understanding the excitation mechanisms of galaxies. The curves used to separate the different regions - HII-like or else excited by an active galactic nucleus (AGN) - have been refined over time but the core technique has not evolved significantly. However, the classification of galaxies based on their emission line ratios really is a multi-dimensional problem. Here we exploit recent software developments to explore the potential of three-dimensional (3D) line ratio diagnostic diagrams. We introduce a specific set of 3D diagrams, the ZQE diagrams, which separate the oxygen abundance and the ionisation parameter of HII region-like spectra, and which also enable us to probe the excitation mechanism of the gas. By examining these new 3D spaces interactively, we define a new set of 2D diagnostics, the ZE diagnostics, which can provide the metallicity of objects excited by hot young stars, and which cleanly separate HII region-like objects from the different classes of AGNs. We show that these ZE diagnostics are consistent with the key log[NII]/H$\alpha$ vs. log[OIII]/H$\beta$ diagnostic currently used by the community. They also have the advantage of attaching a probability that a given object belongs to one class or to the other. Finally, we discuss briefly why ZQE diagrams can provide a new way to differentiate and study the different classes of AGNs in anticipation of a dedicated follow-up study.

21 pages, 15 figures, accepted for publication in ApJ. Due to size limitations, the supplementary STL file for the 3D-printable diagram is available here: http://www.mso.anu.edu.au/~fvogt/online_material.html

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Cited by in corpus (5)

• Imprints of galaxy evolution on H ii regions Memory of the past uncovered by the CALIFA survey
• Probing the Physics of Narrow Line Regions in Active Galaxies II: The Siding Spring Southern Seyfert Spectroscopic Snapshot Survey (S7)
• Oxygen abundance maps of CALIFA galaxies
• A Machine Learning Artificial Neural Network Calibration of the Strong-Line Oxygen Abundance
• 3D Printing Meets Computational Astrophysics: Deciphering the Structure of Eta Carinae's Inner Colliding Winds