Flux extraction based on general regression neural network for two-dimensional spectral image

Abstract

In this paper, one novel method to extract flux from two dimensional spectral images which we observed through LAMOST (Large Area Multi-Object Fiber Spectroscopic Telescope) is proposed. First of all, the spectral images are preprocessed. Then, in the flux extraction algorithm, the GRNN (General Regression Neural Network) and double Gaussian function are employed to simulate the profile of each spectrum in spatial orientation. We perform our experiment, with same radial basis function, by GRNN and RBFNN (Radial Basis Function Neural Network) method. The experimental results show that our method performs higher SNR (Signal Noise Ration) and lower time-consuming that is more applicable in such massive spectral data.