我們試圖通過機器學習技術來改進系外行星探索方法。我們提出了二維捲機神經網路（2D-CNN），用於通過觀測到的光變曲線來進行系外行星凌日檢測。開普勒太空望遠鏡觀測到的光變曲線被使用來學習和測試新的2D-CNN模型。我們將2D-CNN的凌星檢測的標準性、可靠性和整性與1D-CNN的結果進行了比較，並展示了使用2D-CNN的優勢。此方法還可用於從其他凌星觀測的光變曲線中搜索新的凌星，例如TESS。
此外，我們將機器學習技術應用於通過角度差分成像（ADI）技術的直接影像法的系外行星檢測。我們使用VLT/SPHERE的IRDIS所觀測到的數據來進行測試。這裡我們提出了具有殘差學習技術和批量歸一化的神經網路，MWIN5-RB。這可以將ADI方法拍攝到的低質量圖像轉換為高質量圖像，並提高圖像中的系外行星的信噪比（SNR）。

We try to improve the methods of exoplanet detection through machine-learning techniques. We propose two-dimensional convolutional neural network (2D-CNN) for detecting exoplanet transits from observed light curves. We use light curves observed by Kepler space telescope to learn and test new 2D-CNN models. We compare accuracy, reliability, and completeness of transit detections by our 2D-CNN with the results of 1D-CNN and show the advantage of using 2D-CNN. Our method can be employed to search for possible new transits from light curves of other transit survey such as TESS.
Furthermore, we apply machine learning techniques to the detection of exoplanets by direct imaging method with angular differential imaging (ADI) technique. We test on the image data observed by VLT/SPHERE with Infrared Dual-band Imager and Spectrograph (IRDIS). Here we proposed Modified five-layer Wide Inference Network with the Residual learning technique and Batch normalization (MWIN5-RB), which can convert low quality image taken by ADI method into high quality image and increase signal-to-noise-ratio (SNR) of exoplanet in the image.

1 Introduction 1
2 Machine-Learning 5
2.1 Overview of Machine-Learning and Artificial Intelegence . . . . . . . 5
2.2 Logistic Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 Deep-Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.4 Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . 9
2.5 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3 Transit Method 14
3.1 Overview of Transit Method . . . . . . . . . . . . . . . . . . . . . . . 14
3.2 The Data of Kepler Space Telescope . . . . . . . . . . . . . . . . . . . 15
3.3 Injected Planet Signal in Light Curve . . . . . . . . . . . . . . . . . . 17
4 Convolutional Neural Network for Detecting Exoplanet Transit 19
4.1 The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.2 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.2.1 Training, Validation, and Testing Processes . . . . . . . . . . . 23
4.2.2 Signal-to-Noise Ratios . . . . . . . . . . . . . . . . . . . . . . 25
4.2.3 Transit Phase Positions . . . . . . . . . . . . . . . . . . . . . . 26
4.2.4 Folding Periods . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.3 Demonstration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
5 Direct Imaging Method 35
5.1 Overview of Angular Differenrial Imaging . . . . . . . . . . . . . . . . 35
5.2 VLT SPHERE Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
5.3 Injected Planet Image . . . . . . . . . . . . . . . . . . . . . . . . . . 39
5.4 Signal-to-Noise Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
6 Convolutional Neural Network for Denoising Exoplanet Image 43
6.1 The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
6.2 The Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
6.2.1 The Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
6.2.2 The Operation of Layers . . . . . . . . . . . . . . . . . . . . . 47
6.2.3 The Residual . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
6.2.4 The Optimization . . . . . . . . . . . . . . . . . . . . . . . . . 51
6.3 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
6.4 Demonstration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
7 Conclusions 59
7.1 The Conclusion of Convolutional Neural Network for Exoplanet Transit 59
7.2 The Conclusion of Convolutional Neural Network for Direct Imaging
Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

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