Nickel- and iron-rich clumps in planetary nebulae: New discoveries and emission-line diagnostics

TL;DR

This study uses integral field spectroscopy and machine learning to identify and analyze iron and nickel-rich clumps in planetary nebulae, revealing their formation mechanisms, excitation sources, and elemental depletion patterns.

Contribution

It introduces new emission-line diagnostics and applies machine learning to classify excitation mechanisms in planetary nebulae's iron and nickel-rich clumps.

Findings

16 iron- and nickel-rich clumps detected in 7 PNe

New shock/photoionization classification criterion established

Fe and Ni abundances are below solar, indicating dust depletion

Abstract

Integral field spectroscopy (IFS) offers a distinct advantage for studying extended sources by enabling spatially resolved emission maps for several emission lines without the need for specific filters. This study conducts a detailed analysis of iron and nickel emission lines in 12 planetary nebulae (PNe) using integral field unit (IFU) data from MUSE to provide insights into their formation and evolution mechanisms. New diagnostic line ratios, combined with machine-learning algorithms, were used to distinguish excitation mechanisms such as shock and photoionization. Electron densities and elemental abundances were estimated using different atomic data through the PyNeb package. The contribution of fluorescent excitation of nickel lines was also examined. A total of 16 iron- and nickel-rich clumps are detected in seven out of 12 PNe. New clumps are discovered in NGC 3132 and IC 4406. The most prominent lines are [Fe II] 8617 Angstrom and [Ni II] 7378 Angstrom. Both emission lines are observed emanating directly from the low-ionization structures (LIS) of NGC 3242, NGC 7009, and NGC 6153, as well as from clumps in NGC 6369 and Tc 1. Their abundances are found to be below solar values, indicating that a fraction of Fe and Ni remains depleted in dust grains. The depletion factors exhibit a strong correlation over a wide range. A machine-learning approach allows us to classify ten out of 16 clumps as shock-excited and to establish a new shock/photoionization selection criterion: log([Ni II] 7378 Angstrom / H-alpha) and log([Fe II] 8617 Angstrom / H-alpha) greater than -2.20.