Estimation of Stellar Atmospheric Parameters from LAMOST DR8 Low-resolution Spectra with 20 ≤ S/N < 30  

作　　者：Xiangru Li Zhu Wang Si Zeng Caixiu Liao Bing Du Xiao Kong Haining Li 

机构地区：[1]School of Computer Science,South China Normal University,Guangzhou 510631,China [2]School of Mathematical Sciences,South China Normal University,Guangzhou 510631,China [3]Key Laboratory of Optical Astronomy,National Astronomical Observatories,Chinese Academy of Sciences,Beijing 100101,China [4]University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《Research in Astronomy and Astrophysics》2022年第6期204-214,共11页天文和天体物理学研究（英文版）

基　　金：supported by the National Natural Science Foundation of China(grant Nos.11973022,11973049,and U1811464);the Natural Science Foundation of Guangdong Province(No.2020A1515010710);the Youth Innovation Promotion Association of the CAS(id.Y202017)。

摘　　要：The accuracy of the estimated stellar atmospheric parameter evidently decreases with the decreasing of spectral signal-to-noise ratio(S/N)and there are a huge amount of this kind observations,especially in case of S/N<30.Therefore,it is helpful to improve the parameter estimation performance for these spectra and this work studied the(T_(eff),log g,[Fe/H])estimation problem for LAMOST DR8 low-resolution spectra with 20≤S/N<30.We proposed a data-driven method based on machine learning techniques.First,this scheme detected stellar atmospheric parameter-sensitive features from spectra by the Least Absolute Shrinkage and Selection Operator(LASSO),rejected ineffective data components and irrelevant data.Second,a Multi-layer Perceptron(MLP)method was used to estimate stellar atmospheric parameters from the LASSO features.Finally,the performance of the LASSO-MLP was evaluated by computing and analyzing the consistency between its estimation and the reference from the Apache Point Observatory Galactic Evolution Experiment high-resolution spectra.Experiments show that the Mean Absolute Errors of T_(eff),log g,[Fe/H]are reduced from the LASP(137.6 K,0.195,0.091 dex)to LASSO-MLP(84.32 K,0.137,0.063 dex),which indicate evident improvements on stellar atmospheric parameter estimation.In addition,this work estimated the stellar atmospheric parameters for 1,162,760 lowresolution spectra with 20≤S/N<30 from LAMOST DR8 using LASSO-MLP,and released the estimation catalog,learned model,experimental code,trained model,training data and test data for scientific exploration and algorithm study.

关 键 词：fundamental parameters of stars-astronomy data modeling-algorithms 

分 类 号：P152[天文地球—天文学]