Abstract
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 $< \lambda <$ 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.
Abstract (translated)
积分场光谱学（IFS）调查提供了一个独特的新领域，可以同时在空间和分光维度上进行学习，并且可能有助于揭示关于星系演化之前未知的见解。在这项工作中，我们展示了一种新的无监督深度学习框架，使用卷积长短时记忆网络自编码器来对马尼亚（MaNGA）IFS调查中约9000个星系样本中的19条光学发射线（3800Å < λ < 8000Å）的空间和光谱维度进行泛化特征表示的编码。作为一种演示练习，我们在290个活动星系核（AGN）的样本上评估了我们的模型，并突出了某些高度异常AGN的一些科学兴趣特性。
URL
https://arxiv.org/abs/2602.18426
