Deep Multimodal Representation Learning for Stellar Spectra

TL;DR

This paper applies contrastive learning to multimodal stellar data, creating structured, physically meaningful representations that improve stellar parameter estimation and enable cross-modal generation.

Contribution

It introduces a contrastive learning framework for multimodal stellar data, pioneering the integration of Gaia XP coefficients and RVS spectra for enhanced astrophysical analysis.

Findings

Structured representation space with explicit physical meaning

High-quality cross-modal generation of stellar data

Accurate and precise stellar label predictions

Abstract

Recently, contrastive learning (CL), a technique most prominently used in natural language and computer vision, has been used to train informative representation spaces for galaxy spectra and images in a self-supervised manner. Following this idea, we implement CL for stars in the Milky Way, for which recent astronomical surveys have produced a huge amount of heterogeneous data. Specifically, we investigate Gaia XP coefficients and RVS spectra. Thus, the methods presented in this work lay the foundation for aggregating the knowledge implicitly contained in the multimodal data to enable downstream tasks like cross-modal generation or fused stellar parameter estimation. We find that CL results in a highly structured representation space that exhibits explicit physical meaning. Using this representation space to perform cross-modal generation and stellar label regression results in excellent performance with high-quality generated samples as well as accurate and precise label predictions.