Estimation of stellar mass and star formation rate based on galaxy images

作者：Jing Zhong, Zhijie Deng, Xiangru Li, Lili Wang, Haifeng Yang, Hui Li, Xirong Zhao · 发表于：Monthly Notices of the Royal Astronomical Society · 年份：2024 · DOI：10.1093/mnras/stae1271 · 被引用次数：8 · 研究领域：Astronomical Observations and Instrumentation、Astronomy and Astrophysical Research、Stellar, planetary, and galactic studies

ABSTRACT It is crucial for a deeper understanding of the formation and evolution of galaxies in the Universe to study stellar mass (M*) and star formation rate (SFR). Traditionally, astronomers infer the properties of galaxies from spectra, which are highly informative, but expensive and hard to be obtained. Fortunately, modern sky surveys obtained a vast amount of high-spatial-resolution photometric images. The photometric images are obtained relatively economically than spectra, and it is very helpful for related studies if M* and SFR can be estimated from photometric images. Therefore, this paper conducted some preliminary researches and explorations on this regard. We constructed a deep learning model named Galaxy Efficient Network (GalEffNet) for estimating integrated M* and specific star formation rate (sSFR) from Dark Energy Spectroscopic Instrument galaxy images. The GalEffNet primarily consists of a general feature extraction module and a parameter feature extractor. The research results indicate that the proposed GalEffNet exhibits good performance in estimating M* and sSFR, with σ reaching 0.218 and 0.410 dex. To further assess the robustness of the network, prediction uncertainty was performed. The results show that our model maintains good consistency within a reasonable bias range. We also compared the performance of various network architectures and further tested the proposed scheme using image sets with various resolutions and wavelength bands. Furthermore, w...