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研究生： 廖俐雯
Liao, Li-Wen
論文名稱： 新一代光學巡天觀測計畫下的解析星系光度學
Resolved Galaxy Photometry in Next Generation Deep Imaging Surveys
指導教授： 安德魯古柏
Cooper, Andrew
口試委員: 江國興
Kong, Albert
賴詩萍
Lai, Shih-Ping
林俐暉
Lin, Li-Hwai
吳柏鋒
Wu, Po-Feng
學位類別： 博士
Doctor
系所名稱： 理學院 - 天文研究所
Institute of Astronomy
論文出版年： 2023
畢業學年度： 112
語文別： 英文
論文頁數： 136
中文關鍵詞： 星系 、星系形成與演化 、機器學習
外文關鍵詞： galaxy, galaxy formation and evolution, machine learning
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    • 星系形成和演化的過程會受到不同的因素影響，包含氣體吸積、恆星形成、恆星回饋、活躍星系核（AGN）回饋以及星系之間合併也會影響星系最終的樣子。此外，在星系剛形成的最一開始，星系所處的暗物質暈也會影響形成過程。考量其複雜的程度，研究星系形成和演化會需要結合觀測以及模擬雙方的結果。在觀測方面，因為顏色梯度會受到星系內部星星的年齡、金屬含量和塵埃分布的影響，我們認為星系的顏色梯度是可以用來研究星系內部結構的觀測參數。在這項研究中，我們使用DESI Legacy Imaging Surveys的資料測量星系的顏色梯度，並探討其與星系性質的變化。此外，我們使用MaNGA FIREFLY測量星系的年齡、金屬含量和塵埃的梯度。我們進一步將觀測結果與IllustrisTNG模擬進行了比較。結果顯示，觀測到的顏色梯度跟絕對星等的趨勢與模擬結果符合，但顏色梯度與平均顏色不同。這種差異可能是由於IllustrisTNG模擬中活躍星系核的回饋程度太過劇烈。在全面理解了星系的形成和演化之後，我們將研究重點聚焦在矮星系上。然而，對矮星系的研究在很大程度上依賴於紅移。因為儀器的限制，所以現有的光譜儀觀測往往優先考慮較亮的星系。因此，直接使用圖像觀測去估計紅移成為研究較暗星系很有用的工具。在這個研究中，我們使用機器學習技術去估計在Siena Galaxy Atlas catalog裡的星系的光譜紅移。

    Galaxy formation and evolution encompass multiple physical processes, including gas accretion, star formation, stellar feedback, AGN feedback, and mergers. Furthermore, the initial conditions of haloes also influence the formation processes. Given the complexity of this topic, comprehensive studies integrating observations and simulations are necessary. In terms of observations, we propose that color gradients are valuable parameters for unveiling resolved information about galaxies as color gradients reflect intrinsic age, metallicity, and dust gradients within galaxies. In this study, we measure color gradients using the DESI Legacy Imaging Surveys and investigate their variations with galaxy properties. Additionally, we measure age, metallicity, and dust gradients using the FIREFLY MaNGA catalog. We further compared our observational findings with the IllustrisTNG simulations in the simulation aspect. The results demonstrate good agreement between the observed color gradient trends and simulations of absolute magnitude, but discrepancies arise in average color. This discrepancy may be attributed to uncertainties in the AGN feedback modeling within the IllustrisTNG simulations. Having comprehensively understood galaxy formation and evolution, we narrow our research focus to dwarf galaxies. However, studies of dwarf galaxies heavily rely on redshift information. Given that spectroscopic observations tend to prioritize brighter galaxies due to instrumental limitations, photometric redshift estimation becomes a valuable tool for investigating faint galaxies. Therefore, we employ machine learning techniques to estimate photometric redshifts within the SGA catalog.

    Declaration Acknowledgements i 摘要 ii Abstract iii 1 Introduction 1 1.1 Observational point of view . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2 Simulation approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3 Thesis overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.3.1 Colour gradient: observations . . . . . . . . . . . . . . . . . . . . . . 19 1.3.2 Colour gradient: IllustrisTNG simulation . . . . . . . . . . . . . . . . 20 1.3.3 Dwarf galaxies and photometric redshift estimation . . . . . . . . . . . 21 2 Color gradients of low-redshift galaxies in the DESI Legacy Imaging Survey 25 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.2 The Legacy Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.2.1 Sample selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.2.2 Measurement of color gradients . . . . . . . . . . . . . . . . . . . . . 36 2.2.3 1/Vmax weighting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.3 Results for spectroscopic redshift sample . . . . . . . . . . . . . . . . . . . . . 37 2.3.1 color gradient as a function of average color and Mr . . . . . . . . . . 39 2.3.2 Color gradient as a function of M⋆ and sSFR . . . . . . . . . . . . . . 43 2.3.3 Color gradients for red and blue galaxies . . . . . . . . . . . . . . . . 44 2.4 Results for photometric redshift sample . . . . . . . . . . . . . . . . . . . . . 48 2.4.1 Color gradient as a function of average color and Mr . . . . . . . . . . 48 2.4.2 Color gradients for red and blue galaxies . . . . . . . . . . . . . . . . 53 2.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 2.5.1 Color gradients and galaxy formation . . . . . . . . . . . . . . . . . . 54 2.5.2 Comparisons to integral field spectroscopy . . . . . . . . . . . . . . . 60 2.5.3 Positive color gradients in field dwarf galaxies . . . . . . . . . . . . . 65 2.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 A Extreme color gradients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 B Effects of total dust reddening on the color-magnitude diagram . . . . . . . . . 77 C catalog and fitting results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 3 Colour gradient of Illustris TNG100 galaxies 85 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 3.2 Simulation data from IllustrisTNG100 . . . . . . . . . . . . . . . . . . . . . . 87 3.3 Colour gradients of TNG galaxies . . . . . . . . . . . . . . . . . . . . . . . . 88 3.4 Colour gradients as a function of other observables . . . . . . . . . . . . . . . 89 3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 4 Photometric Redshift Estimation and Investigation of the SGA Catalogue 99 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 4.1.1 Photometric redshift . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 4.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 4.2.1 SGA catalog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 4.2.2 Training sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 4.2.3 Photometric Redshift from Zhou et al. (2021) . . . . . . . . . . . . . . 103 4.3 Decision Tree and Random Forest . . . . . . . . . . . . . . . . . . . . . . . . 106 4.3.1 Input Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 4.3.2 Photometric redshift in our training model . . . . . . . . . . . . . . . . 108 4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 4.4.1 SGA galaxies with and without zsdss . . . . . . . . . . . . . . . . . . . 112 4.4.2 Scaling relations in the SGA catalog . . . . . . . . . . . . . . . . . . . 113 4.4.3 Dwarf galaxies candidate in the SGA catalog . . . . . . . . . . . . . . 115 4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 5 Summary 119

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