用无监督网络学习星系光谱的空间-光谱特征，发现异常类星体新线索
Spatio-Spectroscopic Representation Learning using Unsupervised Convolutional Long-Short Term Memory Networks
- 设计卷积LSTM自编码器，联合学习空间与光谱维度特征
- 在9000个星系上提取19条发射线特征，识别出290个类星体异常样本
- 无需标注即可发现潜在天体物理异常，适合天文数据挖掘研究者
积分场光谱巡天（IFS）为同时在空间和光谱维度上学习提供了独特机遇，有助于揭示星系演化的未知规律。本文提出一种基于卷积长短期记忆网络自编码器的无监督深度学习框架，用于编码跨越19条光学发射线（3800Å < λ < 8000Å）的广义特征表示，覆盖来自MaNGA IFS巡天的约9000个星系。作为演示，我们在290个活动星系核（AGN）样本上评估模型，揭示了一些高度异常的AGN的科学有趣特征。
原文摘要 · Abstract (English)
Integral Field Spectroscopy (IFS) surveys offer a unique new landscape in which to learn in both spatial and spectroscopic dimensions and could help uncover previously unknown insights into galaxy evolution. In this work, we demonstrate a new unsupervised deep learning framework using Convolutional Long-Short Term Memory Network Autoencoders to encode generalized feature representations across both spatial and spectroscopic dimensions spanning $19$ optical emission lines (3800A $< λ<$ 8000A) among a sample of $\sim 9000$ galaxies from the MaNGA IFS survey. As a demonstrative exercise, we assess our model on a sample of $290$ Active Galactic Nuclei (AGN) and highlight scientifically interesting characteristics of some highly anomalous AGN.
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