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研究生： 黃于瑄
Huang, Yu-Xuan
論文名稱： 以機器學習法搜尋雙星系統的研究
Searching Binary Stars through Machine Learning Techniques
指導教授： 葉麗琴
Yeh, Li-Chin
口試委員: 江瑛貴
Jiang, Ing-Guey
李金龍
Li, Chin-Lung
學位類別： 碩士
Master
系所名稱： 理學院 - 計算與建模科學研究所
Institute of Computational and Modeling Science
論文出版年： 2020
畢業學年度： 108
語文別： 中文
論文頁數： 59
中文關鍵詞： 雙星系統 、卷積神經網路 、光曲線數據
外文關鍵詞： Binary Stars System, convolutional neural network, light curve data
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    • 為了搜尋雙星系統，我們使用JKTEBOP程式[10]建構資料庫。然後構建了三種不同干擾系數的模型，並訓練它們在卷積神經網路的運算中。光曲線數據做為模型的輸入。我們利用在卷積神經網路中不同的參數建構一個最佳模型，利用此模型在三種不同的干擾背景之下，對它們的數值結果進行了比較和改進。最後結果，我們發現望遠鏡的干擾背景對雙星系統的參數模型有著重要影響。

    In order to search the binary star system, we use the JKTEBOP program [10] to construct the database. Then we construct three models with different noises coefficients and train them in the operation of convolutional neural network. The light curve data is used as the input of the model. We use different parameters in convolutional neural network to construct an optimal model, and use this model to compare and improve their numerical results under three different noises. As a result, we found that the interference background of the telescope has an important influence on the parameter model of the binary system.

    致謝 II 摘要 III Abstract IV Chapter 1 Introduction 2 Chapter 2 Machine Learning 4 2.1 Neural Networks Architecture (NN) 4 2.1.1 The Loss Functions 6 2.1.2 The Activation Functions 7 2.2 Convolutional Neural Network(CNN) 10 2.2.1 Pooling Layer 12 2.2.2 Keras 13 2.3 Eclipsing Binaries Model 15 Chapter 3 Models and Parameters 23 3.1 Activation Functions and Optimizer Methods 23 3.1.1 Two-layers 24 3.1.2 Three-layers 25 3.1.3 Four-layers 26 3.2 Total Data Size 27 Chapter 4 Results 30 4.1 Precision, Recall, and F score 30 4.2 Models 32 4.2.1 Model A 33 4.2.2 Model B 35 4.2.3 Model C 38 4.3 The Comparison and Improvement of Three Models 41 Chapter 5 Conclusions 44 References 45 Appendix A Optimizer 47 Appendix B Program 52

    [1] Aitken, Robert G, The Binary Stars, New York: McGraw-Hill, 1935:1.
    [2] Robert Grant Aitken, The Binary Stars, New York: Dover, 1964, p.1.
    [3] JKTEBOP, The solar-type eclipsing binary system LL Aquarii, Astronomy & Astrophysics, September 2013, Volume 557.
    [4] https://mropengate.blogspot.com/2015/06/ch15-4-neural-network.html
    [5] https://medium.com/%E5%AD%B8%E4%BB%A5%E5%BB%A3%E6%89%8D/%E5%84%AA%E5%8C%96%E6%BC%94%E7%AE%97-3-gradient-descent-with-momentum-52a65550edc0
    [6] Chintarungruangchai, Pattana and Jiang, Guey, Detecting Exoplanet
    Transits through Machine-learning Techniques with Convolutional Neural Networks, Publications of the Astronomical Society of the Pacific, 2019, vol 131, 064502.
    [7] Prs ̌a, A and Guinan, EF and Devinney, EJ and DeGeorge, M and Bradstreet, DH and Giammarco, JM and Alcock, CR and Engle, SG, Artificial intelligence approach to the determination of physical properties of eclipsing binaries. i. the ebai project, The Astrophysical Journal, 2008, vol 687, 542.
    [8] Hinners, Trisha A and Tat, Kevin and Thorp, Rachel, Machine learning techniques for stellar light curve classification, The Astronomical Journal, 2018, vol 156, 7.
    [9] Ketkar, Nikhil and others, Deep Learning with Python, Springer, 2017.
    [10] Southworth, John, JKTEBOP: Analyzing light curves of detached eclipsing binaries, Astrophysics Source Code Library, 2012.

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