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

Generative AI & LLMs
Published: arXiv: 2602.18426v1
Authors

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 $< λ<$ 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.

Paper Summary

Problem
Galaxy evolution is a complex process, and studying it requires analyzing large amounts of data from Integral Field Spectrographs (IFS). However, the sheer volume and dimensionality of this data pose a significant challenge. Astronomers need a way to extract meaningful insights from this data, but traditional methods are limited.
Key Innovation
This research paper presents a new unsupervised deep learning framework that uses Convolutional Long-Short Term Memory Networks (ConvLSTM) to learn feature representations across both spatial and spectroscopic dimensions. This framework, called Spatio-Spectroscopic Representation Learning, is applied to a sample of 9,043 galaxies from the MaNGA IFS survey. The model is able to learn compressed and generalized feature representations, allowing for the identification of anomalous galaxies, including those hosting Active Galactic Nuclei (AGN).
Practical Impact
The practical impact of this research is significant. By developing a framework that can efficiently analyze large amounts of IFS data, astronomers can gain new insights into galaxy evolution. This can lead to a better understanding of the complex processes governing galaxy evolution, such as star formation and the growth of supermassive black holes. Additionally, the ability to identify anomalous galaxies can help scientists understand the diversity of galaxy populations and the underlying physical mechanisms that drive their evolution.
Analogy / Intuitive Explanation
Imagine trying to analyze a massive library of books, each containing a unique combination of words and sentences. Traditional methods would require manually reading each book, but this would be impractical and time-consuming. The Spatio-Spectroscopic Representation Learning framework is like a powerful search engine that can scan the entire library, identify patterns and relationships, and extract meaningful information from the data. This allows scientists to focus on the most interesting and anomalous galaxies, rather than getting lost in the vast amounts of data.
Paper Information
Categories:
astro-ph.GA cs.CV
Published Date:

arXiv ID:

2602.18426v1

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