1. 德州学院计算机与信息学院 德州 253023;2. 德州学院能源与机械学院 德州 253023

P157;

Stellar Population Measurement of LAMOST Galaxies Based on Deep Learning
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1. School of Computer and Information, Dezhou University, Dezhou 253023;2. School of Energy and Machinery, Dezhou University, Dezhou 253023

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摘要:

星系的光谱包含其内部恒星的年龄和金属丰度等信息, 从观测光谱数据中测量这些信息对于深入了解星系的形成和演化至关重要. LAMOST (Large Sky Area Multi-Object Fiber Spectroscopic Telescope)巡天发布了大量的星系光谱, 这些高维光谱与它们的物理参数之间存在着高度的非线性关系. 而深度学习适合于处理多维、海量的非线性数据, 因此基于深度学习技术构建了一个8个卷积层$+$4个池化层$+$1个全连接层的卷积神经网络, 对LAMOST Data Release 7 (DR7)星系的年龄和金属丰度进行自动估计. 实验结果表明, 使用卷积神经网络通过星系光谱预测的星族参数与传统方法基本一致, 误差在0.18dex以内, 并且随着光谱信噪比的增大, 预测误差越来越小. 实验还对比了卷积神经网络与随机森林回归模型、深度神经网络的参数测量结果, 结果表明卷积神经网络的结果优于其他两种回归模型.

Abstract:

A galaxy spectrum contains the information of the age and metallicity distribution of the stars in the galaxy. Measuring the stellar population parameters from the observed spectral data is very important for an in-depth understanding of the formation and evolution of the galaxy. LAMOST (Large Sky Area Multi-Object Fiber Spectroscopic Telescope) has observed a large number of galaxy spectra. These spectra are high-dimensional data, and there is a highly nonlinear relationship between the spectra and their physical parameters. Deep learning is suitable for processing multi-dimensional and massive nonlinear data. Therefore, a convolution neural network with 8 convolution layers +4 pooling layers +1 full connection layer is constructed based on deep learning to automatically estimate the age and metallicity of LAMOST Data Release 7 (DR7) galaxy. The experimental results show that the prediction of stellar population parameters (age and metallicity) using convolution neural network model for galaxy spectra is basically consistent with the parameter values obtained by traditional methods with an accuracy better than 0.18dex. As the signal to noise ratio ($S/N_r$) increases, the dispersion of the differences decreases. We also compare the measurement results of convolutional neural network with random forest regression model and deep neural network. The results show that the convolutional neural network is better than the other two regression models.

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##### 历史
• 收稿日期:2021-11-22
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• 在线发布日期: 2022-09-30
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