Estimating Atmospheric Parameters from LAMOST Low-Resolution Spectra with Low SNR

Xiangru Li, Si Zeng, Zhu Wang, Bing Du, Xiao Kong, Caixiu Liao

Submitted on 13 July 2022


Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) acquired tens of millions of low-resolution stellar spectra. The large amount of the spectra result in the urgency to explore automatic atmospheric parameter estimation methods. There are lots of LAMOST spectra with low signal-to-noise ratios (SNR), which result in a sharp degradation on the accuracy of their estimations. Therefore, it is necessary to explore better estimation methods for low-SNR spectra. This paper proposed a neural network-based scheme to deliver atmospheric parameters, LASSO-MLPNet. Firstly, we adopt a polynomial fitting method to obtain pseudo-continuum and remove it. Then, some parameter-sensitive features in the existence of high noises were detected using Least Absolute Shrinkage and Selection Operator (LASSO). Finally, LASSO-MLPNet used a Multilayer Perceptron network (MLPNet) to estimate atmospheric parameters Teff, log g and [Fe/H]. The effectiveness of the LASSO-MLPNet was evaluated on some LAMOST stellar spectra of the common star between APOGEE (The Apache Point Observatory Galactic Evolution Experiment) and LAMOST. it is shown that the estimation accuracy is significantly improved on the stellar spectra with 10<SNR80. Especially, LASSO-MLPNet reduces the mean absolute error (MAE) of the estimation of Teff, log g and [Fe/H] from (144.59 K, 0.236 dex, 0.108 dex) (LASP) to (90.29 K, 0.152 dex, 0.064 dex) (LASSO-MLPNet) on the stellar spectra with 10<SNR20. To facilitate reference, we release the estimates of the LASSO-MLPNet from more than 4.82 million stellar spectra with 10<SNR80 and 3500 < SNRg 6500 as a value-added output.


Comment: 14 pages, 12 figures, 5 tables

Subjects: Astrophysics - Instrumentation and Methods for Astrophysics; Astrophysics - Astrophysics of Galaxies; Astrophysics - Solar and Stellar Astrophysics