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作者(中文):徐允心
作者(外文):Hsu, Yun-Hsin
論文名稱(中文):史隆巡天第四代 MaNGA計畫:星系相食實錄--星系團最亮星系的多核率
論文名稱(外文):SDSS IV MaNGA: Cannibalism Caught in the Act -- on the Frequency of Occurrence of Multiple Cores in Brightest Cluster Galaxies
指導教授(中文):林彥廷
賴詩萍
指導教授(外文):Lin, Yen-Ting
Lai, Shih-Ping
口試委員(中文):王為豪
安德魯 古柏
口試委員(外文):Wang, Wei-Hao
Cooper, Andrew
學位類別:碩士
校院名稱:國立清華大學
系所名稱:天文研究所
學號:107025505
出版年(民國):110
畢業學年度:110
語文別:英文
論文頁數:98
中文關鍵詞:星系星系團橢圓星系星系形成星系合併
外文關鍵詞:galaxiesgalaxy clusterselliptical and lenticular, cD galaxiesgalaxy formationmerger
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在Λ-冷暗物質宇宙學典範下,宇宙中結構是以階層式形成。星系團最亮星系(Brightest cluster galaxies, 簡稱BCG)位於星系團的中心,正代表了此階層式結構形成的頂點。星系團最亮星系的性質與它們的星系團有強烈關聯,並且它們的形成過程很可能含有星系與星系團形成的重要限制條件。紅移小於0.5時,星系團最亮星系的恆星質量增長在觀測和模擬之間有差異。此研究旨在解決此差異。

我們藉由直接比較星系團最亮星系中心區域的多核率來解決這個差異。我們利用MaNGA計畫(Mapping Nearby Galaxies at APO, 簡稱MaNGA)所提供89個星系團最亮星系的空間解析光譜,以區分合併系統和視線方向隨機重疊的投影,並得到星系團最亮星系多核率之最具統計顯著性的成果。我們開發了一套程式以自動化偵測星系團最亮星系的核,並將此程式使用於最新最大的宇宙學流體動力學模擬IllustrisTNG的300^3立方百萬秒差距(Mpc^3)版本製成的擬真模擬影像。

我們從紅移為0.06-0.15的體積限制樣本(volume-limited sample)測得觀測多核率為0.097±0.036,並從218個模擬的星系團最亮星系測得多核率為0.066±0.018。

我們的觀測結果與模擬結果只有一個標準差的差異。我們還發現多核通常實際上和星系團最亮星系有關聯,而非隨機的重疊投影。將來我們的自動化程式可應用在對於星系團最亮星系的更深影像巡天計畫和更多樣本的無狹縫光譜巡天計畫。
In the ΛCDM cosmological paradigm, structures form hierarchically. Brightest cluster galaxies (BCGs) are located at the center of galaxy clusters, which represent the culmination of this formation. The properties of BCGs are strongly connected with their host clusters, and their growth paths potentially contain important constraints on cluster and galaxy formation. This work aims to solve the discrepancy between observation and simulation on the stellar mass growth of BCGs at z < 0.5.

We tackle this discrepancy by directly comparing the multiple-core frequency (a proxy for merger rate) in the inner regions of BCGs. We take advantage of the spatially resolved spectroscopy of 89 BCGs from Mapping Nearby Galaxies at APO (MaNGA) to distinguish merging systems from chance projections, and present the result of the first IFU survey of the multiple-core frequency of BCGs. We develop a pipeline to automatically detect cores and apply it to realistic synthetic images based on the (300 Mpc)^3 volume version of the cosmological hydrodynamical simulation IllustrisTNG.

We obtain an observational multiple-core frequency of 0.097±0.036 from a volume-limited sample at z = 0.06-0.15, and a multiple-core frequency of 0.066±0.018 from 218 simulated BCGs.

Our observation results show only a 1 sigma discrepancy from a leading hydrodynamical simulation. We also find multiple cores are most often associated with their BCGs rather than chance projections. In the future, our pipeline can be applied to data from deeper imaging surveys and larger slitless spectroscopy surveys of BCGs.
Abstract (Chinese) I
Abstract II
Acknowledgements III
Contents VI
List of Figures IX
List of Tables XVII
1 Introduction 1
1.1 A distinct galaxy population: brightest cluster galaxies (BCGs) . . 1
1.2 A discrepancy in BCG stellar mass growth . . . . . . . . . . . . . . 2
1.3 Multiple-core frequency . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 The structure of this work . . . . . . . . . . . . . . . . . . . . . . . 6
2 An IFU Survey of Multiple-core Frequency of Brightest Cluster
Galaxies 7
2.1 The Galaxy and Cluster Samples . . . . . . . . . . . . . . . . . . . 7
2.1.1 The Cluster Sample . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.2 Identi cation of BCGs . . . . . . . . . . . . . . . . . . . . . 9
2.1.3 Photometry . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1.4 Maximum projected distance and IFU Coverage . . . . . . . 11
2.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.1 Analysis Plan . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.2 Identifying Multiple-cores in Images . . . . . . . . . . . . . . 12
2.2.3 Identifying Merging Systems . . . . . . . . . . . . . . . . . . 14
2.2.4 Flux Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.2.5 Volume-limited sample . . . . . . . . . . . . . . . . . . . . . 18
2.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 23
3 Multiple-core Frequency of Brightest Cluster Galaxies in TNG300 27
3.1 The Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2 The Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2.1 Synthetic Images . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2.2 Photometry . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.2.3 Identifying Multiple-cores . . . . . . . . . . . . . . . . . . . 32
3.2.4 Flux Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 33
4 Conclusion and Prospects 38
Appendices 40
A Sample Selection 41
A.1 Unbiased selection . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
A.2 Excluded objects and systems . . . . . . . . . . . . . . . . . . . . . 45
A.2.1 Excluded objects in the observational sample at the BCG identifi cation stage . . . . . . . . . . . . . . . . . . . . . . . 45
A.2.2 Excluded objects in the observational sample due to image quality issues . . . . . . . . . . . . . . . . . . . . . . . . . . 50
A.2.3 Excluded objects in the simulated sample . . . . . . . . . . . 53
B Notable objects 56
B.1 Notable objects in the observational sample . . . . . . . . . . . . . 56
B.2 Notable objects in the simulated sample . . . . . . . . . . . . . . . 58
C Consistency of Photometry between Ellipse and PyMorph 60
D Tables of the BCGs and the detected cores 68
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