應用卷積類神經網路對小行星光變曲線圖之週期類別判別處理;Classification for the Rotation Periods of Asteroids Using the Convolution Neural Network

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題名:	應用卷積類神經網路對小行星光變曲線圖之週期類別判別處理;Classification for the Rotation Periods of Asteroids Using the Convolution Neural Network
作者:	林唐正;Lin, Tang-Cheng
貢獻者:	資訊工程學系
關鍵詞:	小行星週期;光變曲線;卷積類神經網路;監督式學習;Asteroid rotation periods;Light-curve;Convolutional neural network;Supervised learning
日期:	2019-07-26
上傳時間:	2019-09-03 15:39:25 (UTC+8)
出版者:	國立中央大學
摘要:	在天文學中，我們常以光變曲線來找尋小行星的自旋週期。光變曲線是天體的亮度對應於時間的變化的一個函數。傳統的方法是對光變曲線作週期分析，並將光變曲線對可能的週期進行疊合後，再以天文學家自身專業與經驗來判斷該光變曲線圖是何種週期類別。但是近年來由於天文觀測技術的提升，獲取的天文資料數量也大幅增加，傳統人工判斷的方法曠日廢時不再可行，因此需要設計有效的方法來協助天文學家判斷該光變曲線的週期類別。本論文將以泛星計畫 (Pan-STARRS, Panoramic Survey Telescope and Rapid Response System) 所觀測出的光變曲線作為實驗的資料來源，以二階的傅立葉函數擬合泛星計畫的小行星光變曲線，並得到一個 reduce 2對應頻率的頻譜圖，在該頻譜圖中可以發現一至數個頻率有極小值的reduce 2，將該光變曲線對這幾個頻率進行疊合而得一個疊合光變曲線，並以此疊合光變曲線作為週期類別之判斷。由於小行星的形狀不是正圓球體，在疊合光變曲線中若呈現雙峰狀並且與基線擬合程度夠高，便可以認定找到該小行星之週期，若呈現單峰狀則認定找到小行星之半週期，其餘的情況便認定該光變曲線無法找到小行星週期。在此狀況下，雙峰狀的疊合光變曲線類似W，而單峰狀的則類似V或倒V，因此本論文導入監督式學習(supervised learning)技術並使用卷積類神經網路(CNN, convolutional neural network)設計一個類似人工判斷週期類別之工具，以疊合光變曲線為輸入，最後輸出該疊合光變曲線之類別，即W、V、或無類別。實驗結果顯示，此方式的準確度高且判別速度遠快於傳統的人工瀏覽。 ;In astronomical researches, the rotation periods of asteroids can be derived from their light curves which are the brightness as a function of time. Traditionally, a periodical analysis is performed on the light curve, and then astronomers determine the category of a possible period according to the folded light curve (i.e., a light curve folded to a particular period). This process was relied on human inspection, but it becomes very formidable due to the advancement in the technology of astronomical observation in the last decade that increases the volume of astronomical data set dramatically. Therefore, manual inspection is no longer feasible, and it is necessary to adopt an automatic method to replace the aforementioned time-consuming human review process. In this research, we use the asteroid light curves obtained from the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS). The light curves are fitted using the second-order Fourier series to find the possible rotation periods from the periodogram (i.e., the reduced 2 as a function of period). Then, a folded light curve of a certain period is generated. When a folded light curve shows a clear trend with a double-peak feature (i.e., similar to W), it is identified as a full rotation period. If a folded light curve only shows a single peak, a half rotation period is suggested (i.e., similar to V). The other cases are seen as no period found. Therefore, we deployed a deep learning technology, using convolutional neural network (CNN) as a network architecture, to construct a model to classify the folded light curves to W, V, and other shapes. From the study, we found that our model can precisely and yet much more effectively recognize a result consistent with that of human inspection.
顯示於類別:	[資訊工程研究所] 博碩士論文

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