Spatio-Spectroscopic Representation Learning using Unsupervised Convolutional Long-Short Term Memory Networks
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910028244451328 |
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| author | Mantha, Kameswara Bharadwaj Fortson, Lucy Sankar, Ramanakumar Scarlata, Claudia Lintott, Chris Kruk, Sandor Walmsley, Mike Dickinson, Hugh Masters, Karen Simmons, Brooke Smethurst, Rebecca |
| author_facet | Mantha, Kameswara Bharadwaj Fortson, Lucy Sankar, Ramanakumar Scarlata, Claudia Lintott, Chris Kruk, Sandor Walmsley, Mike Dickinson, Hugh Masters, Karen Simmons, Brooke Smethurst, Rebecca |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_18426 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Spatio-Spectroscopic Representation Learning using Unsupervised Convolutional Long-Short Term Memory Networks Mantha, Kameswara Bharadwaj Fortson, Lucy Sankar, Ramanakumar Scarlata, Claudia Lintott, Chris Kruk, Sandor Walmsley, Mike Dickinson, Hugh Masters, Karen Simmons, Brooke Smethurst, Rebecca Astrophysics of Galaxies Computer Vision and Pattern Recognition 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. |
| title | Spatio-Spectroscopic Representation Learning using Unsupervised Convolutional Long-Short Term Memory Networks |
| topic | Astrophysics of Galaxies Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.18426 |