Spatio-Spectroscopic Representation Learning using Unsupervised Convolutional Long-Short Term Memory Networks

Kameswara Bharadwaj Mantha, Lucy Fortson, Ramanakumar Sankar, Claudia Scarlata, Chris Lintott +6 more
2/20/2026
astro-ph.GAcs.CV

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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 $< λ<$ 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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Cite this paper

@article{mantha2026spatiospectroscopic,
  title  = {Spatio-Spectroscopic Representation Learning using Unsupervised Convolutional Long-Short Term Memory Networks},
  author = {Kameswara Bharadwaj Mantha and Lucy Fortson and Ramanakumar Sankar and Claudia Scarlata and Chris Lintott and Sandor Kruk and Mike Walmsley and Hugh Dickinson and Karen Masters and Brooke Simmons and Rebecca Smethurst},
  year   = {2026},
  eprint = {2602.18426},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2602.18426v1}
}

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