Estimation of age and metallicity for galaxies based on multi-modal deep learning

¹ School of Computer and Information, Dezhou University, Dezhou 253023, China
² School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China
³ International Centre of Supernovae, Yunnan Key Laboratory, Kunming 650216, China

^★ Corresponding author: jsjxwll@126.com

Received: 14 January 2025
Accepted: 22 April 2025

Abstract

Aims. This study is aimed at deriving the age and metallicity of galaxies by proposing a novel multi-modal deep learning framework. This multi-modal framework integrates spectral and photometric data, offering advantages in cases where spectra are incomplete or unavailable.

Methods. We propose a multi-modal learning method for estimating the age and metallicity of galaxies (MMLforGalAM). This method uses two modalities: spectra and photometric images as training samples. Its architecture consists of four models: a spectral feature extraction model (ℳ₁), a simulated spectral feature generation model (ℳ₂), an image feature extraction model (ℳ₃), and a multi-modal attention regression model (ℳ₄). Specifically, ℳ₁ extracts spectral features associated with age and metallicity from spectra observed by the Sloan Digital Sky Survey (SDSS). These features are then used as labels to train ℳ₂, which generates simulated spectral features for photometric images to address the challenge of missing observed spectra for some images. Overall, ℳ₁ and ℳ₂ provide a transformation from photometric to spectral features, with the goal of constructing a spectral representation of data pairs (photometric and spectral features) for multi-modal learning. Once ℳ₂ is trained, MMLforGalAM can then be applied to scenarios with only images, even in the absence of spectra. Then, ℳ₃ processes SDSS photometric images to extract features related to age and metallicity. Finally, ℳ₄ combines the simulated spectral features from ℳ₂ with the extracted image features from ℳ₃ to predict the age and metallicity of galaxies.

Results. Trained on 36278 galaxies from SDSS, our model predicts the stellar age and metallicity, with a scatter of 1σ = 0.1506 dex for age and 1 σ = 0.1402 dex for metallicity. Compared to a single-modal model trained using only images, the multi-modal approach reduces the scatter by 27% for age and 15% for metallicity.