A closer look at the origin of LINER emission and its connection to evolved stars with a machine learning classification scheme
Ahmad Nemer, Ivan Katkov, Jospeh Gelfand, Changhyun Cho

TL;DR
This paper uses machine learning to analyze galaxy spectra and suggests that LINER emission is likely powered by evolved stars, specifically p-AGB stars, rather than active galactic nuclei.
Contribution
It introduces a neural network-based encoder to identify LINERs from spectra and provides evidence linking LINERs to evolved stellar populations.
Findings
LINERs can be identified from stellar continuum alone.
Evolved low-mass stars are consistent with LINER ionizing sources.
LINER emission is more likely due to p-AGB stars than AGN activity.
Abstract
Identifying the dominant ionizing sources in galaxies is essential for understanding their formation and evolution. Traditionally, spectra are classified based on their dominant ionizing source using strong emission lines and Baldwin, Phillips, \& Terlevich (BPT) diagrams. The ionizing source is traditionally determined by the emission line ratios using the BPT diagrams. Low-Ionization Nuclear Emission-line Regions (LINERs) are a class of ionizing mechanisms that is observationally identified but with a poorly understood origin, unlike the case of star forming regions and active galactic nuclei (AGN). LINERs, typically found in early-type galaxies, are often associated with low-luminosity AGN activity but may also be powered by aging stellar populations, particularly post-Asymptotic Giant Branch (p-AGB) stars. In this study, we employ a machine-learning-based encoder, Spender, to…
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Taxonomy
TopicsAstronomy and Astrophysical Research · Stellar, planetary, and galactic studies · Astronomical Observations and Instrumentation
