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研究生： 吳冠賢
Wu, Kuan-Hsien
論文名稱： 在多維星等空間識別年輕恆星物體
Identifying Young Stellar Objects in Multi-dimensional Magnitude Space
指導教授： 賴詩萍
Lai, Shih-Ping
口試委員: 呂聖元
Liu, Sheng-Yuan
平野尚美
Hirano, Naomi
學位類別： 碩士
Master
系所名稱： 理學院 - 物理學系
Department of Physics
論文出版年： 2022
畢業學年度： 110
語文別： 英文
論文頁數： 65
中文關鍵詞： 年輕恆星物體 、恆星生成
外文關鍵詞： Young Stellar Object, Star Formation
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    • 恆星生成速率(SFR)是規範恆星形成理論之關鍵指標。精確的年輕恆星物體(YSO)分類對於得到準確恆星生成速率是不可或缺的。我們構建了一道分類流水線利用多波段光度測量來分辨主序後星、恆星、星系與年輕恆星物體。根據先前觀測或理論仔細的定義主序後星、恆星、星系之物體稠密區域，物體落在餘下區域即為YSO。與傳統使用雙色圖及顏色-星等圖分類法相比，新的分類
    方法利用物體自然定義邊界而非人為判斷。應用此分類流水線至兩個史匹哲遺留計畫︰從分子核心至行星生成盤、古爾德帶探測(GB)的目錄，移除了849個先前方法識別之YSO並新識別出3925個YSO（佔先前方法YSO之126%）。應用此分類流水線至史匹哲強化影像目錄，我們識別了36804個年輕恆星物體，移除了31650個由機器學習方法(Chiu et al. 2021)識別之YSO並 新識別出14009個YSO。以上與先前方法之巨大的差異來自於兩點︰(1)先前的恆星樣本中有來自點擴散方程擬合的巨大的光度誤差(2)新的主序後星分類。然而多數第一類及平坦類YSO維持不變，除了在GB目錄中增加了20%，先前的重大差異主要來自主序後星以及第三類YSO，並不影響SFR之計算。我們的YSO分類流水線可以直接應用於將來的詹姆斯·韋伯太空望遠鏡之觀測，將提供在當前樣本不足之高密度分子雲區更多的資料。

    Star formation rate is a key indicator to constrain star formation theories. To obtain an accurate star formation rate, precise classification of young stellar objects (YSO) is indispensable.
    In this work, we construct a classification pipeline to classify evolved stars, stars, galaxies, and YSOs, solely based on their photometry measurements from multiple bands. We carefully define the object-populated regions for evolved stars, stars, and galaxies based on previous observations or theoretical models. Then, we identify sources as YSOs, if they do not fall in the evolved star, star, and galaxy regions. Compared to the traditional methods that generally use CCD or CMD to classify objects, the advantage of our method is that boundaries are defined naturally with object samples instead of defined artificially with different considerations. We apply this pipeline to catalogs obtained by two Spitzer Legacy Programs: From Molecular Cores to Planet Forming Disks (c2d) and Gould Belt's Survey (GB), which removes 849 previously identified YSOs and identifies 3925 new YSOs which is 126\% of previous identified YSOs. Applying our pipeline to the Spitzer Enhanced Imaging Products (SEIP) which includes all available Spitzer detections, we identify 36804 YSOs in total, which removes 31650 YSOs identified by previous machine learning results (Chiu et al. 2021) and identifies 14009 new YSOs. The huge difference between our identification and previous studies comes from two reasons: (1) large photometry errors caused by PSF fitting in previous star samples and (2) a new evolved stars object type is introduced. Nevertheless, most of class I and flat sources remain unchanged except in the GB regions where we find 20\% more class I and flat sources; a major difference occurs in the identification of stars, evolved stars, and class III YSOs which do not affect the star formation rate calculations. Our new pipeline for YSO identification can be directly applied to the upcoming JWST measurements, providing a better determination of star formation rates in high-density molecular clouds that currently has incomplete YSO samples.

    Contents Abstract (Chinese) . . . . . . . . . . . . . . . . . . . . . . . . I Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . IV Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . VIII List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . XII 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1 Source of Data . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1.1 c2d . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.2 Gould Belt’s Survey (GB) . . . . . . . . . . . . . . . . . . 5 2.1.3 SEIP . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.4 UKIDSS . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.5 SIMBAD . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.6 NED . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Data Preparation . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.1 c2d and GB Catalogs in This Work . . . . . . . . . . . . . . 8 2.2.2 SEIP Catalog in This Work . . . . . . . . . . . . . . . . . 9 3 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.1 Multi-dimensional Magnitude Space . . . . . . . . . . . . . . 12 3.1.1 Finding Object-populated Region Boundary . . . . . . . . . . 14 3.2 Pre-classification Pipeline . . . . . . . . . . . . . . . . . 15 3.2.1 Unknown, Faint, Bright Object Classifier . . . . . . . . . . 16 3.3 Main Classification Pipeline . . . . . . . . . . . . . . . . . 17 3.3.1 Binning Process . . . . . . . . . . . . . . . . . . . . . . 18 3.3.2 Evolved Star Classifier . . . . . . . . . . . . . . . . . . 18 3.3.3 Star Classifier . . . . . . . . . . . . . . . . . . . . . . 19 3.3.4 Galaxy Classifier . . . . . . . . . . . . . . . . . . . . . 20 3.3.5 Re-classification Process . . . . . . . . . . . . . . . . . 22 3.4 Post-classification Pipeline for YSO . . . . . . . . . . . . . 23 3.4.1 Nearby Galaxy Classifier . . . . . . . . . . . . . . . . . . 23 3.5 Appendix: Object Samples . . . . . . . . . . . . . . . . . . . 24 3.5.1 Binned Multi-dimensional Magnitude Space . . . . . . . . . . 24 3.5.2 Object SED Samples in this work . . . . . . . . . . . . . . 24 3.5.3 Object Samples in Magnitude-magnitude Diagram . . . . . . . 28 4 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.1 Spectroscopically Confirmed YSO catalogs . . . . . . . . . . . 29 4.1.1 Comparison of An et al. (2011) [1] . . . . . . . . . . . . . 30 4.1.2 Comparison of Furlan et al. (2016) [8] . . . . . . . . . . . 30 4.2 c2d catalog . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.2.1 Comparison of Evans et al. (2007) [5] . . . . . . . . . . . 32 4.2.2 Comparison of Hsieh & Lai (2013) [14] . . . . . . . . . . . 34 4.3 GB catalog . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.3.1 Comparison of Dunham et al. (2015) [4] . . . . . . . . . . . 35 4.4 SEIP Catalog . . . . . . . . . . . . . . . . . . . . . . . . . 36 4.4.1 Comparison of SCAO (Chiu et al. 2021 [3]) . . . . . . . . . 37 4.4.2 Comparison of Marton et al. (2019) [20] . . . . . . . . . . 38 4.4.3 Comparison of SPICY (Kuhn et al. 2021 [17]) . . . . . . . . 39 4.5 YSO Cross-matched with NED Galaxy . . . . . . . . . . . . . . 40 4.6 Appendix: Online Data . . . . . . . . . . . . . . . . . . . . 42 5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.1 YSO Class Distribution . . . . . . . . . . . . . . . . . . . . 45 5.1.1 YSO Class Distribution in c2d Catalog . . . . .. . . . . . . 46 5.1.2 YSO Class Distribution in GB Catalog . . . . . . . . . . . . 47 5.1.3 YSO Class Distribution in SEIP Catalog . . . . . . . . . . . 49 5.2 Star Formation in c2d and GB Regions . . . . . . . . . . . . . 50 5.2.1 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . 53 6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

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