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.

翻译：积分场光谱巡天为在空间与光谱维度上进行学习提供了独特的新领域，有助于揭示星系演化中先前未知的规律。本研究提出了一种新型无监督深度学习框架，利用卷积长短期记忆网络自编码器，对来自MaNGA积分场光谱巡天的约9000个星系样本中跨越19条光学发射线（3800Å < λ < 8000Å）的空间与光谱维度进行广义特征表征编码。作为示范性应用，我们在290个活动星系核样本上评估了模型性能，并重点揭示了若干高度异常活动星系核在科学上具有研究价值的特征。