基于卷积神经网络的恒星光谱型和光度型的分类模型
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1. 南京邮电大学计算机学院 南京 210023;2. 江苏省大数据安全与智能处理重点实验室 南京 210023;3. 中国科学院国家天文台南京天文光学技术研究所望远镜新技术研究室 南京 210042

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P152;

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国家自然科学基金项目(U1931207)资助


Classification Model of Stellar Spectral Type and Luminosity Type Based on Convolution Neural Network
Author:
Affiliation:

1. College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023;2. Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing 210023;3. Department of Telescope's New Technology, Nanjing Institute of Astronomical Optics & Technology, National Astronomical Observatories, Chinese Academy of Sciences, Nanjing 210042

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    摘要:

    恒星光谱分类是天文学中一个重要的研究问题. 对于已经采集到的海量高维恒星光谱数据的分类, 采用模式匹配方法对光谱型分类较为成功, 但其缺点在于标准恒星模版之间的差异性在匹配实际观测数据中不能体现出来, 尤其是当需要进行光谱型和光度型的二元分类时模版匹配法往往会失败. 而采用谱线特征测量的光度型分类强烈地依赖谱线拟合的准确性. 为了解决二元分类的问题, 介绍了一种基于卷积神经网络的恒星光谱型和光度型分类模型(Classification model of Stellar Spectral type and Luminosity type based on Convolution Neural Network, CSSL_CNN). 这一模型使用卷积神经网络来提取光谱的特征, 通过注意力模块学习到了重要的光谱特征, 借助池化操作降低了光谱的维度并压缩了模型参数的数量, 使用全连接层来学习特征并对恒星光谱进行分类. 实验中使用了大天区面积多目标光纤光谱天文望远镜(Large Sky Area Multi-Object Fiber Spectroscopy Telescope, LAMOST)公开数据集Data Release 5 (DR5, 用了其中71282条恒星光谱数据, 每条光谱包含了3000多维的特征)对该模型的性能进行验证与评估. 实验结果表明, 基于卷积神经网络的模型在恒星的光谱型分类上准确率达到92.04%, 而基于深度神经网络的模型(Celestial bodies Spectral Classification Model, CSC_Model)只有87.54%的准确率; CSSL_CNN在恒星的光谱型和光度型二元分类上准确率达到83.91%, 而模式匹配方法MKCLASS仅有38.38%的准确率且效率较低.

    Abstract:

    Star classification is an important topic in astronomy. For the classification of the massive high-dimensional stellar spectral data that has been collected, the pattern matching method is more successful for spectral classification, but its disadvantage is that the differences between standard star templates cannot be reflected in matching actual observed data. Especially when it comes to the classification of both spectral types and luminosity types, the template matching method often fails. Moreover, the classification of luminosity types based on spectral feature measurement strongly depends on the accuracy of spectral fitting. In order to solve the problem of classification based on spectral type and luminosity type, a Classification model of Stellar Spectral type and Luminosity type based on Convolution Neural Network (CSSL_CNN) is introduced. This model uses a convolutional network to extract features of the spectra, adds attention blocks to focus on learning important features, uses a pooling operation for dimensionality reduction, compressing the number of parameters of the model, and the fully connected layer is used to learn features and classify stars. The Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST) public data set Data Release 5 (DR5) was used in the experiment to verify and evaluate the performance of the model. We used 71282 spectra from DR5, and each spectrum contains more than 3000 features. The experimental results show that the accuracy of our model reaches 92.04% in classification of spectral types, while a Celestial bodies Spectral Classification Model (CSC_Model) based on the deep neural network only reaches 87.54%, and the accuracy of our model is 83.91% in binary classification of spectral and luminosity types, while MKCLASS, a pattern matching method, only has the accuracy of 38.38%, and its efficiency is much lower.

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洪舒欣,邹志强,徐灵哲.基于卷积神经网络的恒星光谱型和光度型的分类模型[J].天文学报,2021,62(5):48. HONG Shu-xin, ZOU Zhi-qiang, XU Ling-zhe. Classification Model of Stellar Spectral Type and Luminosity Type Based on Convolution Neural Network[J]. Acta Astronomica Sinica,2021,62(5):48.

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  • 收稿日期:2020-12-15
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  • 在线发布日期: 2021-10-11
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