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

Kameswara Bharadwaj Mantha; Lucy Fortson; Ramanakumar Sankar; Claudia Scarlata; Chris Lintott; Sandor Kruk; Mike Walmsley; Hugh Dickinson; Karen Masters; Brooke Simmons; Rebecca Smethurst

arXiv · 2602.18426

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

Kameswara Bharadwaj Mantha·Lucy Fortson·Ramanakumar Sankar·Claudia Scarlata·Chris Lintott·Sandor Kruk·Mike Walmsley·Hugh Dickinson·Karen Masters·Brooke Simmons·Rebecca Smethurst

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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 $< \lambda <$ 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.

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Kameswara Bharadwaj Mantha, Lucy Fortson, Ramanakumar Sankar, Claudia Scarlata, Chris Lintott, Sandor Kruk, Mike Walmsley, Hugh Dickinson, Karen Masters, Brooke Simmons, Rebecca Smethurst. 2026-02-20. Spatio-Spectroscopic Representation Learning using Unsupervised Convolutional Long-Short Term Memory Networks. https://arxiv.org/abs/2602.18426

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