重力透鏡和交互作用星系的資料探勘

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：9

、訪客IP：3.135.190.232

姓名

陳詩湧(Sze-Yeong Tan) 查詢紙本館藏

畢業系所

天文研究所

論文名稱

重力透鏡和交互作用星系的資料探勘
(Data Mining for Gravitational Lenses and Interacting Galaxies)

相關論文

★ 土衛六「泰坦」離子球層的化學-動力學模型	★ KBOs星體碰撞與生命及行星大氣起源
★ 行星狀星雲形態之多光譜波段觀測	★ 木衛一埃歐鈉雲噴流之結構與時間變化
★ 早期太陽系系統中KBOs的形成與碰撞演化	★ 彗星2001A2 （LINEAR）的光度觀測
★ SDSS之RR Lyrae候選變星之確認觀測	★ 銀河系核心及盤面的隨機恆星形成歷史
★ 宇宙射線中的氦原子核能譜	★ 小行星對於地球原始海水的貢獻
★ 行星狀星雲Hα結構之分析	★ 在星系團中的相對論性電子和SZ效應
★ 在疏散星團中尋找系外行星與變星	★ 原恆星吸積盤動態模擬與氣體固態粒子作用初步探討
★ 大型EKBO(Quaoar, Ixion, 2004DW)的自轉週期和表面顏色的測量	★ 利用反投影法研究SN1006之宇宙射線加速機制

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

依據廣義相對論，光線的行進路徑會天受重力影響而彎曲。若遙遠的星體與地球之間有大質量的物質存在，在類似光線經過透鏡聚焦的情形下，我們會觀測到圓弧狀的天體或是雙重甚至多重影像。這種放大現象可以幫助我們更了解高紅移且微弱的星系。
目前大部份的天文學家用目視或基於星系團的稠密度來搜尋重力透鏡事件，而我們則利用星體形狀資訊進行物體的辨識。由於這種新的思維方法是基於形狀參數，理論上是可以偵測出由暗星系團或暗物質所造成的重力透鏡現象。
我們根據圖像的『第二階中心矩』守恆作出『橢圓形誤差』的形狀參數之定義，並使用它來篩選星體。接下是要對可疑的星體做線性和弧形回歸。最後根據以上兩種回歸的誤差來判別它是否是重力透鏡作用的現象。由於我們所使用的回歸法和形狀參數，與圖像的矩函數有關係，在資料有效的運用下使得整個運算過程很快。本方法除了可以挑出重力透鏡現象也可以篩選出交互作用星系，它們對了解宇宙論以及星系及星系團的演化都是非常重要的。

摘要(英)

The scientific operations of space telescopes and ground-based facilities worldwide have produced a flood of astronomical data waiting to be analyzed. Thus the development of fast and efficient system is in urgent demand for the purpose of data mining.
The discovery of gravitational lensing events and interacting galaxies are very important in the study of cosmology. However, both types of structures are relatively rare and often hidden in the mountain of images. For these reasons, we have developed an automatic system to identify these objects from image archives by shape analysis.
First, candidates are selected with the shape parameter defined by our method and a line and an arc are then fitted to these potential candidates. From error analysis the best shape can be identified. The algorithm developed in this work has been tested on two of the gravitational lensing events found in the RCS and proved to be successful. Furthermore, it has also been applied to a portion of the RCS data set, which consists of 210 images and dozens of interacting galaxies have been found.

關鍵字(中)

★ 重力透鏡
★ 交互作用星系
★ 矩分析
★ 圖形識別
★ 孤型回歸演算法

關鍵字(英)

★ pattern recognition
★ arc fitting algorithm
★ moment analysis
★ gravitational Lensing
★ interacting galaxies

論文目次

1 Introduction 1 1.1 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 The Basic of Gravitational Lensing . . . . . . . . . . . . . . . . . 3 1.2.1 Thin Lens Approximation . . . . . . . . . . . . . . . . . . 3
1.2.2 Lens Equation . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.3 The Critical Surface-Mass Density and Einstein Radius . 4
1.2.4 Image Magnifcations . . . . . . . . . . . . . . . . . . . . 5
1.2.5 Singular Isothermal Sphere . . . . . . . . . . . . . . . . . 6
1.2.6 Effective Lensing Potential . . . . . . . . . . . . . . . . . 7
1.2.7 The Fermat Principle . . . . . . . . . . . . . . . . . . . . 7
1.3 Gravitational Lensing as a Cosmological Probe . . . . . . . . . . 8
2 Methodology 11
2.1 Basic Idea - Recognition by Fitting Error . . . . . . . . . . . . . 11
2.2 Moment Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2.1 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.2 Shape Representation Using Moments . . . . . . . . . . . 12
2.2.3 Invariant Moments . . . . . . . . . . . . . . . . . . . . . . 13
2.3 Image Segmentation by Localized Thresholding . . . . . . . . . . 14
2.3.1 Estimation of Sky Background . . . . . . . . . . . . . . . 14
2.3.2 Local Thresholding . . . . . . . . . . . . . . . . . . . . . . 14
2.4 Shape Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.4.1 Best-Fit Ellipse . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4.2 Dissimilarity . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4.3 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.5 Selection of Candidates by Using Dissimilarity and Eccentricity . 18
2.6 Estimation of the Circular Arc Center and Its Radius . . . . . . 19
2.6.1 Landau's Algorithm . . . . . . . . . . . . . . . . . . . . . 20
2.6.2 Thomas-Chan's Algorithm . . . . . . . . . . . . . . . . . . 22
3 Data Processing 24 3.1 The Red-Sequence Survey(RCS) . . . . . . . . . . . . . . . . . . 24
3.1.1 Incidence of Strong-Lensing in the RCS Survey . . . . . . 24
3.1.2 RCS 0224.5-0002 and RCS 1419.2+5326 . . . . . . . . . . 28
3.1.2.1 RCS 0224.5-0002 . . . . . . . . . . . . . . . . . . 29
3.1.2.2 RCS 1419.2+5326 . . . . . . . . . . . . . . . . . 30
3.2 Data Processing Pipeline . . . . . . . . . . . . . . . . . . . . . . . 30 3.3 Results of Test Images . . . . . . . . . . . . . . . . . . . . . . . . 33 3.3.1 Result of RCS 0224-0002 . . . . . . . . . . . . . . . . . . 33
3.3.2 Result of RCS 1419.2+5326 . . . . . . . . . . . . . . . . . 33
3.3.3 Parameter Spaces of The Detected Arcs . . . . . . . . . . 34
4 Results and Discussion 36 4.1 Results of 0920 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
A Principal Moments of Inertia 43
B Geometrical Moments of a General Ellipse 44
C Derivation of the Landau's Algorithm 45
D Derivation for the Thomas-Chan's Algorithm 47

參考文獻

[1] D. Walsh, R. Carswell, and R. Weymann, 0957+561 A,B: Twin Quasistel- lar Objects or Gravitational Lens? Nature , pp. 381-384, 1979.
[2] S. Randall and C. Sarazin, The contribution of Extreme Ultraviolet from Clusters of Galaxies to the Cosmic Ionizing Background, The Astrophysical
Journal , vol. 548, pp. 60-67, 2001.
[3] M. Bartelmann, A. Huss. J.M. Colberg, A. Jenkins, and F. Pearce, Arc Statistics with Realistic Cluster Potential, Astronomy and Astrophysics , vol. 330, pp. 1-9, 1998.
[4] M. Gladders, H. Henk, H. Yee, P. Hall, and L. Barrientos, The Incidence of Strong-Lensing Clusters in the Red-Sequence Cluster Survey, The As-
trophysical Journal , vol. 593, pp. 48-55, 2003.
[5] M. Gladders and H Yee, A New Method For Galaxy Cluster Detection. I. The Algorithm, The Astrophysical Journal , vol. 120, pp. 2148-2162, 2000.
[6] M. Hu, Visual Pattern Recognition by Moment Invariants, IRE Trans.
Info. Theory , vol.8, pp. 179-187, 1962.
[7] J. Jarvis and J. Tyson, FOCAS - Faint Object Classi˝cation and Analysis System, Astronomical Journal , vol. 86, pp. 476-495, 1981.
[8] S. Andreaon, G. Gargiulo, G. Longo, R. Tagliaferri, and N. Capuano, I. Applications of Neutral Networks to Object Detection and star/galaxy Classi˝cation, Monthly Notice of the Royal Astronomical Society , vol. 319, pp. 700-716, 2000.
[9] M. Serra-Ricart, V. Gailan, L. Garrido, and I. Perez-Fournon, Faint Ob- ject Classi˝cation Using Arti˝cial Neural Networks, Astronomy and As-
trophysics , vol. 115, pp. 195-207, 1996.
[10] P.B. Stetson, Daophot - A Computer Program for Crowded-˝eld Stellar Photometry, Astronomical Society of the Paci˝c, Publications(ISSN 0004-
6280) , vol. 99, march 1987, pp. 191-222, 1987.[11] H. Yee, A Faint-Galaxy Photometry and Image-Analysis System, As-
tronomical Society of the Paci˝c, Publications(ISSN 0004-6280) , vol. 103, April 1991, pp. 396-411, 1991.
[12] E. Bertin and S. Arnouts, SExtractor: Software for Source Extraction,
Astronomy and Astrophysical Supplements , pp. 117-393, 1996.
[13] M. Kendall and K. Stuart, 1977, The Advanced Theory of Statistics, Vol. 1, Distribution Theory, Charles Gri°n & Co., London .
[14] D.H. Davies and D. R. Dance, Automatic Computer Detection of Clustered Calci˝cation in Digital Mammograms, Phys. Med. Biol. , vol. 35, no. 8, pp. 1111-1118, 1990.
[15] K.L. Lau, W.C. Siu and N.F. Law, Best-˝t Ellipse Concept From The Sec- ond Order Moments for Object Retrieval, First International Conference
on Information Technology and Applications , vol. 25, pp. 8-14, 1976.
[16] I. Kasa, A Circle Fitting Procedure and its Error Analysis, IEEE Trans.
Instrumentation and Measurement , vol. 25, pp. 8-14, 1976.
[17] A. Thom, A Statistical Examination of The Megalithic Sites in Britain,
J. Roy. Statist. Soc. Ser. A General , vol. 118, pp. 275-295, 1955.
[18] V. Karimaki, E˙ective Circle Fitting for Particle Trajectories, Nucl. In-
stru. Methods Phys. Research. , pp. 187.-191, 1991.
[19] U.M. Landau, Estimation of a Circular Arc Center and Its Radius, Com-
puter Vision. Graphics and Image Processing , vol. 38, pp. 317-326, 1987.
[20] 1S.M. Thomas and Y.T Chan, A Simple Approach for the Estimation of Circular Arc Center and its Radius, Computer Vision, Graphics and
Image Processing , vol. 45, pp. 362-370, 1989.
[21] H. Henk, H. Yee, and M. Gladders, Constraints on
m and
8 from Weak Lensing in Red-Sequence Cluster Survey Fields, The Astrophysical Jour-
nal , vol. 577, pp. 595-603, 1989.
[22] M. Gladders, H. Yee, and E. Ellingson, Discovery of a z = 0.77 Galaxy Cluster with Multiple, Bright, Strong-Lensing Arcs, The Astrophysical
Journal , vol. 123, pp. 1-9, 2002.
[23] D. Clowe, G. Luppino, N. Kaiser, J. Henry, and I. Gioia, Weak Lensing by two Z approximately 0.8 Clusters of Galaxies, Astrophysical Journal
Letters , vol. 497, pp. L61-L64, 1998.
[24] M. Franx, G. Illingworth, D. Kelson, P. van Dokkum, and K. Tran, A Pair of Lensed Galaxies at z = 4.92 in the Field of CL 1358+62, Astrophysical
Journal Letters , vol. 486, pp. L75-L78, 1997.
[25] J. Wanbsganss, Gravitational Lensing in Astronomy, Living Reviews , 2001.
[26] R. Narayan and M. Bartelmann, Lectures on Gravitational Lensing, e-
Print Archive , astro-ph/9606001, 1998.
[32] R. Mukundan, Moment functions in Image Analysis Theory and Applica- tions, World Scienti˝c Publishing Co. Pte. Ld , 1998.

指導教授

葉永烜(Wing-Huen Ip)

審核日期

2004-4-30

推文