ESNet: Estimating Stellar Parameters from LAMOST Low-Resolution Stellar Spectra - Kun Wang; Bo Qiu; A-li Luo; Fuji Ren; Xia Jiang (Universe, 2023)
Stellar parameters are estimated through spectra and are crucial in studying both stellar evolution and the history of the galaxy. To extract features from the spectra efficiently, we present ESNet (encoder selection network for spectra), a novel architecture that incorporates three essential modules: a feature encoder (FE), feature selection (FS), and feature mapping (FM). FE is responsible for extracting advanced spectral features through encoding. The role of FS, on the other hand, is to acquire compressed features by reducing the spectral dimension and eliminating redundant information. FM comes into play by fusing the advanced and compressed features, establishing a nonlinear mapping between spectra and stellar parameters. The stellar spectra used for training and testing are obtained through crossing LAMOST and SDSS. The experimental results demonstrate that for low signal-to-noise spectra (0–10), ESNet achieves excellent performance on the test set, with mean absolute error (MAE) values of 82 K for <semantics>Teff</semantics> (effective temperature), 0.20 dex for <semantics>log<mspace width="0.166667em"></mspace>g</semantics> (logarithm of the gravity), and 0.10 dex for <semantics>[Fe/H]</semantics> (metallicity). The results indeed indicate that ESNet has an excellent ability to extract spectral features. Furthermore, this paper validates the consistency between ESNet predictions and the SDSS catalog. The experimental results prove that the model can be employed for the evaluation of stellar parameters.