Spatio-Spectroscopic Representation Learning using Unsupervised Convolutional Long-Short Term Memory Networks
2026-02-20 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors used a new type of machine learning model to study detailed data from about 9,000 galaxies. Their approach looks at both where things are in the galaxies and the light they give off across many colors. They focused especially on 290 galaxies with active black holes and found some unusual examples that could be interesting for future research. This work helps show how combining advanced computer methods with galaxy data can find new patterns.
Integral Field SpectroscopyConvolutional Long-Short Term Memory NetworkAutoencoderOptical Emission LinesMaNGA SurveyGalaxy EvolutionActive Galactic NucleiUnsupervised Deep LearningAnomaly Detection
Authors
Kameswara Bharadwaj Mantha, Lucy Fortson, Ramanakumar Sankar, Claudia Scarlata, Chris Lintott, Sandor Kruk, Mike Walmsley, Hugh Dickinson, Karen Masters, Brooke Simmons, Rebecca Smethurst
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.