Spatio-Spectroscopic Representation Learning using Unsupervised Convolutional Long-Short Term Memory Networks
摘要
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 optical emission lines (3800A 8000A) among a sample of galaxies from the MaNGA IFS survey. As a demonstrative exercise, we assess our model on a sample of Active Galactic Nuclei (AGN) and highlight scientifically interesting characteristics of some highly anomalous AGN.
引用
@article{arxiv.2602.18426,
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},
journal= {arXiv preprint arXiv:2602.18426},
year = {2026}
}
备注
This manuscript was previously submitted to ICML for peer review. Reviewers noted that while the underlying VAE-based architecture builds on established methods, its application to spatially-resolved IFS data is promising for unsupervised representation learning in astronomy. This version is released for community visibility. Reviewer decisions: Weak accept and Weak reject (Final: Reject)