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

Author:

Zhong Jing1,Deng Zhijie2,Li Xiangru2,Wang Lili3,Yang Haifeng4,Li Hui2,Zhao Xirong1

Affiliation:

1. School of Artificial Intelligence, South China Normal University , Foshan 528225 , Peoples Republic of China

2. School of Computer Science, South China Normal University , No. 55 West of Yat-sen Avenue, Guangzhou 510631 , Peoples Republic of China

3. School of Computer and Information, Dezhou University , Dezhou 253023 , Peoples Republic of China

4. School of Computer Science and Technology, Taiyuan University of Science and Technology , Taiyuan 030024 , Peoples Republic of China

Abstract

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, we conducted applicability analysis on galaxies of various sizes, redshifts, and morphological types. The results indicate that our model performs well across galaxies with various characteristics and indicate its potentials of broad applicability.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Guangdong Province

Publisher

Oxford University Press (OUP)

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