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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/63949Full metadata record
| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 朱有花(You-Hua Chu) | |
| dc.contributor.author | Chen-Fatt Lim | en |
| dc.contributor.author | 林征發 | zh_TW |
| dc.date.accessioned | 2021-06-16T17:24:05Z | - |
| dc.date.available | 2020-04-16 | |
| dc.date.copyright | 2020-04-16 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-03-16 | |
| dc.identifier.citation | Abdurro’uf, Akiyama, M. 2018, MNRAS, 479, 5083
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/63949 | - |
| dc.description.abstract | 本論文的研究對象主要是在次毫米波觀測到的星系，探討它們的
物理性質，從而進一步瞭解它們的生成以及演化。這些星系是經由15 米的詹姆士克拉克麥斯威爾望遠鏡中鑲嵌的SCUBA-2 儀器觀測得到。 本論文結合了SCUBA-2 最新的大型觀測計畫（STUDIES）以及所有 SCUBA-2 在COSMOS 巡天場裡的公開資料，建構了有史以來最深的 450 微米影像，其觀測靈敏度在最深的區域大約是0.6 斯基。本論文 的第一部分探討了這些星系的物理性質，例如：紅移、恆星生成率、 恆星質量、紅外光度以及塵埃輻射溫度。對於紅移小於3 以及紅外光 度大於太陽光度十的十二次方倍的450 微米星系，其塵埃輻射溫度並沒有隨著 紅移而有所演化。但是它們的塵埃輻射溫度與其於恆星生成主星序的 距離成正向關係。此外，塵埃輻射溫度也與其可見光形態有關聯，符 合了次毫米星系中的星暴成因是藉由星系間碰撞及併吞引發的。本論 文也計算了450 微米星系的光度函數以及它們的恆星生成率密度。在 紅移0 到2 之間，大於太陽光度十的十二次方倍的450 微米星系貢獻於恆星生 成率密度逐漸提升，並主導了紅移大於2 的區間。本論文的第二部分 利用了450 微米觀測到的次毫米波星系以及K-譜帶觀測到的非次毫米 波星系做為研究的基底，並透過機器學習的分類方法發展了一個分類 器。這個機器學習的分類器可以被利用於大範圍的COSMOS 巡天場 （1.6 平方度），從而在整個COSMOS 巡天場內判斷出哪些星系是次毫 米波星系的候選星系。針對這些次毫米波候選星系在紅移0.5 到3 的 區間，本論文進行了星系的兩點相關函數的分析，從而發現它們的暈 質量大約是太陽質量的二乘十的十三次方倍。此外，它們的暈質量並沒有顯示 出強烈的紅移演化關係。本研究旨在擴大我們對次毫米波星系生成與 演化的瞭解。 | zh_TW |
| dc.description.abstract | In my thesis, I studied a sample of sub-millimeter galaxies (SMGs), aiming
at unveiling their physical properties and providing insight in the physical processes shaping galaxy formation and evolution. I based the analysis on the 450-μm data obtained from the SCUBA-2 camera on the 15-m James Clerk Maxwell Telescope (JCMT). By combining a new SCUBA-2 imaging survey from the ongoing JCMT Large Program - SCUBA-2 Ultra Deep Imaging EAO Survey (STUDIES) and all the archival data in the CANDELS/ COSMOS field, I constructed an extremely deep single-dish image across an area of 300 arcmin^2 at 450μm (S450μm ≃ 0.6 mJy beam^-1). This image is the deepest ever observed at 450μm. In the first part of my thesis, I probed the physical properties of the 450-μm-selected galaxies, such as redshift, star-formation rate (SFR), stellar mass (M *), infrared luminosity (LIR), and dust temperature (Td). I did not find a redshift evolution in dust temperature for sources with LIR > 10^12 L⊙ at z < 3. I found a moderate correlation between the dust temperature and the deviation from the SFR–M * relation. The increase in dust temperature also correlates with optical morphology, which is consistent with merger-triggered starbursts in submillimeter galaxies. I constructed the infrared luminosity functions of the 450-μm sources and measure their comoving SFR densities (SFRDs). I discovered that the contribution of the LIR > 10^12 L⊙ population to the SFRD rises dramatically from z = 0 to 2 and dominates the total SFRD at z ≳ 2. In the second part of my thesis, I developed a machine-learning classifier using optical-to-near-infrared colors from the 450-μm-selected SMGs and a sample of K-band-selected non-SMGs. I employed the trained machine-learning classifier to the general COSMOS field (1.6 deg^2) and identified a sample of SMG candidates with similar colors to the training SMG sample. I measured the two-point autocorrelation functions and found that the SMG candidates reside in massive halos of ≃ (2+-0.5) 10^13 h^-1M⊙ across the redshift range of z = 0.5–3.0. I did not find evidence of downsizing that is suggested by recent observational studies. The work presented in this dissertation contributes to widening our understanding of the evolution of SMGs. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T17:24:05Z (GMT). No. of bitstreams: 1 ntu-109-D04244001-1.pdf: 12100736 bytes, checksum: 5a688a53fbba55c5f16edec0f550af4a (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 誌謝ii
摘要iii Abstract v 1 Introduction 1 1.1 A typical galactic spectral energy distribution . . . . . . . . . . . . . . . 1 1.2 Infrared/Sub-millimeter Observations . . . . . . . . . . . . . . . . . . . 1 1.3 Local Infrared-luminous Galaxies . . . . . . . . . . . . . . . . . . . . . 3 1.4 Sub-millimeter Galaxies . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.5 The 450-mm observations . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.6 Thesis Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2 Multi-wavelength Properties and Luminosity Functions 10 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2 Multi-wavelength Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2.1 SCUBA-2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2.2 Ancillary Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.3 Source Extraction and Counterpart Identification . . . . . . . . . . . . . 22 2.3.1 Source Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.3.2 Counterpart Identification . . . . . . . . . . . . . . . . . . . . . 24 2.3.3 Unidentified Sources . . . . . . . . . . . . . . . . . . . . . . . . 25 2.4 Deriving Physical Parameters . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.1 AGN Contamination . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.2 Redshift . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.4.3 Stellar Mass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 2.4.4 Infrared Luminosity . . . . . . . . . . . . . . . . . . . . . . . . 36 2.4.5 Dust Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.4.6 Ultraviolet-continuum Slope and Ultraviolet Luminosity . . . . . 40 2.4.7 SFRs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 2.4.8 Radio Power at 1.4 GHz . . . . . . . . . . . . . . . . . . . . . . 42 2.5 The Nature of SCUBA-2 450-mm-selected Sources . . . . . . . . . . . . 43 2.5.1 The Star-formation Main Sequence . . . . . . . . . . . . . . . . 44 2.5.2 Td–LIR Correlation . . . . . . . . . . . . . . . . . . . . . . . . . 50 2.5.3 IRX–bUV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 2.6 Infrared Luminosity Function . . . . . . . . . . . . . . . . . . . . . . . . 59 2.6.1 1/Vmax Method . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 2.6.2 Likelihood Method . . . . . . . . . . . . . . . . . . . . . . . . . 62 2.6.3 Comparison with Other Observations . . . . . . . . . . . . . . . 65 2.6.4 Comparison with Models . . . . . . . . . . . . . . . . . . . . . . 66 2.7 The Obscured Star-formation History . . . . . . . . . . . . . . . . . . . 68 2.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 3 Spatial Clustering and Halo Masses 75 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 3.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 3.2.1 Main Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 3.2.2 Ancillary Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 3.2.3 Training Sample . . . . . . . . . . . . . . . . . . . . . . . . . . 82 3.3 Machine-Learning Methodology . . . . . . . . . . . . . . . . . . . . . . 84 3.3.1 Performance Measures of Classification . . . . . . . . . . . . . . 84 3.3.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 3.3.3 Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . 86 3.3.4 Tuning Hyper-parameters . . . . . . . . . . . . . . . . . . . . . 87 3.3.5 Algorithms Comparison . . . . . . . . . . . . . . . . . . . . . . 88 3.4 Verifications of the SMG Candidates . . . . . . . . . . . . . . . . . . . . 89 3.5 Comparison Samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 3.5.1 Rest-frame NUV-r-J Color . . . . . . . . . . . . . . . . . . . . . 93 3.5.2 Physical Properties of SMG Candidates and Comparison Samples 96 3.6 Clustering and Halo Mass . . . . . . . . . . . . . . . . . . . . . . . . . . 99 3.6.1 Two Point Auto-correlation Function . . . . . . . . . . . . . . . 101 3.6.2 Dark-matter Halo Mass . . . . . . . . . . . . . . . . . . . . . . . 103 3.6.3 Clustering Signals . . . . . . . . . . . . . . . . . . . . . . . . . 104 3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 4 Conclusions 109 5 Future Works 111 5.1 A More Complete Picture of SMGs . . . . . . . . . . . . . . . . . . . . 111 5.2 Machine-learning De-blending Procedure for Far-infrared Photometry . . 111 5.3 Star-formation History of Dusty Population . . . . . . . . . . . . . . . . 112 A Monte Carlo Simulation 113 B Confusion Noises 116 C Long Tables 118 D Clustering Results of SMG Candidates Identified by Other Algorithms 129 Bibliography 131 | |
| dc.language.iso | en | |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 宇宙大尺度結構 | zh_TW |
| dc.subject | 星系光度函數 | zh_TW |
| dc.subject | 星系演化 | zh_TW |
| dc.subject | 星系形成 | zh_TW |
| dc.subject | 次毫米波星系 | zh_TW |
| dc.subject | 高紅移星系 | zh_TW |
| dc.subject | machine learning | en |
| dc.subject | high-redshift galaxy | en |
| dc.subject | galaxy formation | en |
| dc.subject | galaxy evolution | en |
| dc.subject | galaxy luminosity function | en |
| dc.subject | large-scale structure of universe | en |
| dc.subject | sub-millimeter galaxy | en |
| dc.title | 多波段性質、星系光度函數以及大尺度結構群聚於450微米星系的研究 | zh_TW |
| dc.title | Multi-wavelength properties, luminosity functions, and clustering measurements of 450-μm-selected galaxies | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.coadvisor | 王為豪(Wei-Hao Wang) | |
| dc.contributor.oralexamcommittee | 平下博之(Hiroyuki Hirashita),林彥廷(Yen-Ting Lin),陳建州(Chian-Chou Chen) | |
| dc.subject.keyword | 次毫米波星系,高紅移星系,星系形成,星系演化,星系光度函數,宇宙大尺度結構,機器學習, | zh_TW |
| dc.subject.keyword | sub-millimeter galaxy,high-redshift galaxy,galaxy formation,galaxy evolution,galaxy luminosity function,large-scale structure of universe,machine learning, | en |
| dc.relation.page | 149 | |
| dc.identifier.doi | 10.6342/NTU202000685 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-03-16 | |
| dc.contributor.author-college | 理學院 | zh_TW |
| dc.contributor.author-dept | 天文物理研究所 | zh_TW |
| Appears in Collections: | 天文物理研究所 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| ntu-109-1.pdf Restricted Access | 11.82 MB | Adobe PDF |
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