Parameter Estimation Model for Stellar Spectral Data based on RDN

Abstract

The temperature, gravity and other key parameters of stars change during their evolution, and finding the relationship between these parameters and the life cycle of stars has always been the research focus in astrophysics. How to estimate the parameters of massive spectral data more accurately is of great significance for studying the properties of stars. However, the current conventional parameter estimation methods have problems such as exploding gradient, vanishing gradient and mismatch of newly added stellar parameters in the face of explosive growth of astronomical data, resulting in low parameter estimation accuracy. The Residual Dense neural network (RDN) model proposed in this paper is mainly improved based on two more advanced neural networks, ResNet and DenseNet. The core of RDN is a new Residual Dense Block (RDB), which includes two modules: residual and dense. The purpose of the residual module is to learn the residual between input and output and add the identity map to it, which aims to solve the problem of vanishing gradient and exploding gradient in deep network training. The dense module is where each layer is directly connected to all the layers before it, allowing better utilization of gradients and feature reuse. Its main purpose is feature extraction. The proposed model was trained on the preprocessed LAMOST DR7 dataset, making uncertainty predictions for 17 stellar parameters in LAMOST DR7 spectra with a signal-to-noise ratio (SNR) equal to or greater than 10. The results show that the proposed model has high estimation accuracy and solves the problems existing in previous methods. Compared with ResNet, DenseNet and StarNet, the key indicators such as the mean absolute error of RDN are optimized.