GALAH巡天：使用自动编码器光谱模拟的发射线星特征
The GALAH survey: Characterization of emission-line stars with spectral modelling using autoencoders
摘要：
我们提出了一种神经网络自动编码器结构，能够从观察到的光谱中提取基本的潜在光谱特征，然后从这些特征中重建光谱。由于使用一组非特殊光谱进行训练，网络能够再现高信噪比的光谱，即使它们存在于观察到的光谱中，也不会显示任何光谱特性。以这种方式产生的光谱被用来确定各种发射特征之间的光谱获得的多次调查使用的HERMES摄谱仪在英澳望远镜。通过直接比较观察到的和生成的光谱来识别发射特征。使用所描述的比较程序，我们发现了10，364个候选光谱，其具有由不同物理机制产生的不同程度的H $α$ /H $β$发射分量。这些光谱的一小部分属于重复观测，显示其发射轮廓的时间变化。在发射光谱中，我们发现了具有附近稀薄气体（通过[NII]和[SII]线的发射来识别）的贡献的对象，该稀薄气体在4004光谱中被识别，这些光谱并不都被识别为具有H $α$发射。已确定的发射线天体的位置与多个已知的年轻恒星所在区域相吻合。同样，探测到的星云发射光谱与在红色全天空合成照片中观察到的可见星云云相吻合。
英文摘要：
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$α$/H$β$ emission component produced by different physical mechanisms. A fraction of those spectra belongs to the repeated observation that shows temporal variability in their emission profile. Among emission spectra, we find objects that feature contributions of a nearby rarefied gas (identified through the emission of [NII] and [SII] lines) that was identified in 4004 spectra, which were not all identified as having H$α$ emission. Positions of identified emission-line objects coincide with multiple known regions that harbour young stars. Similarly, detected nebular emission spectra coincide with visually-prominent nebular clouds observable in the red all-sky photographic composites.
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