The GALAH survey: Characterization of emission-line stars with spectral modelling using autoencoders
Klemen \v{C}otar, Toma\v{z} Zwitter, Gregor Traven, Joss, Bland-Hawthorn, Sven Buder, Michael R. Hayden, Janez Kos, Geraint F. Lewis,, Sarah L. Martell, Thomas Nordlander, Dennis Stello, Jonathan Horner, Yuan-Sen, Ting, Maru\v{s}a \v{Z}erjal

TL;DR
This paper introduces a neural autoencoder that extracts spectral features to identify emission-line stars, discovering over ten thousand candidates and analyzing their physical mechanisms and associations with star-forming regions.
Contribution
The study develops a novel autoencoder-based spectral modeling approach that effectively detects emission features and identifies a large sample of emission-line stars from survey data.
Findings
Identified 10,364 candidate emission-line spectra.
Discovered emission from nearby gas in 4004 spectra.
Found emission objects correlate with star-forming regions.
Abstract
We present a neural network autoencoder structure that is able to extract essential latent spectral features from observed spectra and then reconstruct a spectrum from those features. Because of the training with a set of unpeculiar spectra, the network is able to reproduce a spectrum of high signal-to-noise ratio that does not show any spectral peculiarities even if they are present in an observed spectrum. Spectra generated in this manner were used to identify various emission features among spectra acquired by multiple surveys using the HERMES spectrograph at the Anglo-Australian telescope. Emission features were identified by a direct comparison of the observed and generated spectra. Using the described comparison procedure, we discovered 10,364 candidate spectra with a varying degree of H/H emission component produced by different physical mechanisms. A fraction of…
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