Spatio-Spectroscopic Representation Learning using Unsupervised Convolutional Long-Short Term Memory Networks
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Abstract (arXiv)
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 ~ 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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References · 6
- 2016Layer Normalizationcited by 5,669
- 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
- 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,436
- 2022Radio Galaxy Zoo: Using semi-supervised learning to leverage large unlabelled data-sets for radio galaxy classification under data-set shiftcited by 8
- 2020Semi-supervised Learning of Galaxy Morphology using Equivariant Transformer Variational Autoencoderscited by 1
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