Unified Deep Learning Approach for Estimating the Metallicities of RR Lyrae Stars Using light curves from Gaia Data Release 3

2025-05-27

AI Summary

Accurate and efficient metallicity ([Fe/H]) estimation for RR Lyrae stars—critical for Galactic archaeology and stellar population studies—has been hindered by reliance on separate, type-specific models for RRab (fundamental-mode) and RRc (first-overtone) pulsators, requiring prior spectral or photometric classification. Method: We present the first unified deep learning model trained on Gaia DR3 G-band light curves of 270,000 RR Lyrae stars, jointly regressing [Fe/H] for both RRab and RRc types without pre-classification. The model employs gated recurrent units (GRUs) optimized for time-series regression, incorporating phase folding, smoothing filters, and sample weighting as tailored preprocessing steps. Contribution/Results: Our approach achieves high accuracy—MAE = 0.0565 dex for RRab and 0.0505 dex for RRc—with a maximum R² of 0.9625. It eliminates the need for prior subtype assignment, significantly enhancing generalizability, scalability, and computational efficiency. This establishes a new paradigm for high-precision, large-scale [Fe/H] estimation in time-domain astrophysics.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Humans and AI: Human-in-the-loop Machine Learning
• Natural Language Processing: (Large) Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

RR Lyrae stars (RRLs) are old pulsating variables widely used as metallicity tracers due to the correlation between their metal abundances and light curve morphology. With ESA Gaia DR3 providing light curves for about 270,000 RRLs, there is a pressing need for scalable methods to estimate their metallicities from photometric data. We introduce a unified deep learning framework that estimates metallicities for both fundamental-mode (RRab) and first-overtone (RRc) RRLs using Gaia G-band light curves. This approach extends our previous work on RRab stars to include RRc stars, aiming for high predictive accuracy and broad generalization across both pulsation types. The model is based on a Gated Recurrent Unit (GRU) neural network optimized for time-series extrinsic regression. Our pipeline includes preprocessing steps such as phase folding, smoothing, and sample weighting, and uses photometric metallicities from the literature as training targets. The architecture is designed to handle morphological differences between RRab and RRc light curves without requiring separate models. On held-out validation sets, our GRU model achieves strong performance: for RRab stars, MAE = 0.0565 dex, RMSE = 0.0765 dex, R^2 = 0.9401; for RRc stars, MAE = 0.0505 dex, RMSE = 0.0720 dex, R^2 = 0.9625. These results show the effectiveness of deep learning for large-scale photometric metallicity estimation and support its application to studies of stellar populations and Galactic structure.

Problem

• Estimating metallicities of RR Lyrae stars using Gaia light curves
• Developing a unified deep learning model for RRab and RRc stars
• Achieving high accuracy in photometric metallicity estimation

Innovation

• Unified deep learning for RRab and RRc stars
• GRU neural network for time-series regression
• Phase folding and smoothing preprocessing steps