高紅移恒星形成星系的研究

安芳霞

(中國科學院紫金山天文臺南京210033)

星系是組成宇宙的基石, 其形成與演化是天體物理研究的重要內容.星系中的恒星形成活動是星系成長和演化的主要驅動力之一.已有的星系巡天給出比較一致的宇宙恒星形成歷史: 宇宙的恒星形成密度從高紅移一直增加到紅移z ～2，隨后按指數率下降直到z=0.系統(tǒng)地研究宇宙恒星形成峰值時期恒星形成星系的性質對我們理解并限制星系形成與演化的理論模型至關重要.

論文的第2章和第3章描述我們基于窄帶觀測技術研究高紅移發(fā)射線星系的工作.在第1個工作中我們應用“雙窄帶”巡天技術, 探測同一紅移(z=2.24)、同一觀測場(ExtendedChandraDeep Field South)的Lyα和Hα發(fā)射體.我們比較了Lyα光度/光度密度和修正了塵埃消光的Hα光度/光度密度，并據此估算了紅移z=2.24處單個星系和宇宙整體的Lyα光子逃逸概率.我們的研究發(fā)現: (1)高紅移恒星形成星系中電離氣體和恒星連續(xù)譜之間塵埃消光的差異隨星系恒星形成率的增加而增大; (2) Lyα光子逃逸概率與星系的塵埃消光反相關; (3)紅移z=2.24處宇宙總體的Lyα光子逃逸概率為(3.7±1.4)%;我們的工作首次指出不同的塵埃消光改正方法對高紅移恒星形成星系的研究具有顯著影響.在第2部分工作中, 我們應用窄帶巡天探測到的發(fā)射線星系來尋找高紅移宇宙空間的“原星系團”以及其周圍的大尺度結構.我們的初步結果為: (1)對比平均場, 我們選定的兩個紅移z=2.24的致密區(qū)域BOSS(Baryon Oscillation Spectroscopic Survey) 1244和BOSS1542中Hα發(fā)射線候選體的數密度δg11;(2) BOSS1542場中Hα發(fā)射體的分布展現出了明顯的纖維狀大尺度結構.

研究表明在宇宙恒星形成峰值時期, 極亮紅外星系的恒星形成率占宇宙總量的一半.我們在第4章和第5章描述了這類極端星系的物理性質.我們首先利用高空間分辨率的干涉陣觀測數據構建了訓練樣本，并發(fā)展了機器學習方法來證認單天線亞毫米波段探測源的多波段對應體.我們的研究結果為:(1)我們機器學習方法的召回率為(77.2±4.7)%, 精確度為(82.0±4.9)%, 結合射電證認, 召回率可以提高至85%; (2)訓練樣本的自測試和一系列獨立測試證實了我們方法的成功性; (3)我們的方法可以成功證認出那些亞毫米輻射比較彌散或者/以及比較暗弱的亞毫米星系的對應體, 即使它們的亞毫米輻射低于干涉陣觀測的探測閾值.這表明我們發(fā)展的方法有效解決了亞毫米研究中單天線亞毫米源的低空間分辨率對后續(xù)研究的限制.我們的研究工作也是首次將機器學習的方法應用于亞毫米星系的研究, 提高了研究的效率.我們將該機器學習方法應用于COSMOS (Cosmic Evolution Survey)場中最新的單天線亞毫米巡天(S2COSMOS)探測到的亞毫米源, 并成功證認出了約1200個單天線亞毫米源的光學/近紅外/射電波段的對應體.基于這個精確的亞毫米星系對應體樣本, 我們研究了亞毫米星系的多波段性質.我們證實了機器學習方法證認的亞毫米星系的對應體可以完備地反映亞毫米星系的物理性質, 比如它們趨向于在高紅移宇宙空間, 星系中正在進行恒星形成率高達102M⊙·yr?1的大質量星暴活動.同時我們將該方法應用于整個COSMOS場, 并從中選出了約9000個沒有被單天線亞毫米觀測探測到但是被機器學習方法歸類為亞毫米星系對應體的近紅外源.基于這個統(tǒng)計上足夠大的樣本, 我們研究了亞毫米星系的成團性.通過比較亞毫米星系和同一宇宙時期以及低紅移宇宙空間中其他類型天體的成團性的強度, 我們可以研究亞毫米星系和其他類型天體的演化關聯(lián).我們的這些研究工作促進了對高紅移恒星形成星系物理性質的認識, 為理解星系尤其是大質量星系的形成與演化提供了更為精確的限制.

A Study of High-redshift Star-forming Galaxies

AN Fang-xia

(Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 210033)

Galaxies are fundamental building blocks of the Universe.Understanding their detailed formation and evolution is a major task in modern astronomy.Star formation is a key process that regulates galaxy formation and evolution.A consistent picture is emerging from modern galaxy surveys, whereby the star formation rate density peaks atz ～2, and exponentially declines to the present day.Systematic study of star-forming galaxies at the peak epoch holds the key to our understanding of galaxy formation and evolution.

Chapters 2 and 3 of this dissertation present our studies based on the narrow-band imaging techniques.In Chapter 2,we present the results from the dual narrow-band imaging surveys in the search of Lyαand Hαemitters at the same redshift (z=2.24) and in the same field (ExtendedChandraDeep Field South).We estimate the mean Lyαescape fraction atz=2.24 by comparing the observed ratio between the Lyαluminosity to total luminosity and to the extinction-corrected Hαluminosity fraction.We find that: (1) the difference in the dust attenuation between ionized gas and stellar continuum increases with star formation rate; (2) there is an anti-correlation between Lyαescape fraction and dust attenuation; (3) the global Lyαescape fraction atz=2.24 is (3.7±1.4)%; For the first time we point out that different extinction corrections significantly affect the studies of highredshift star-forming galaxies.In the second project presented in this dissertation, we use the narrow-band-selected emission-line galaxies to identify the protoclusters, and map the cosmic structure around them in the early universe.Our preliminary results show that: (1)comparing to the random field, the galaxy overdensity in our two overdense fields BOSS(Baryon Oscillation Spectroscopic Survey) 1244 and BOSS1542 isδg11; (2) a large-scale filamentary structure is shown by the Hαemitters in the BOSS1542.

The extremely luminous infrared galaxies contributed almost half of the cosmic star formation at the epoch of the peak star formation.In our third and forth projects, we study the physical properties of this extremal galaxy extreme population through the submillimeter observations.We develop a machine-learning method to identify the possible multi-wavelength counterparts of submillimeter detected source in panoramic, single-dish submillimeter surveys using a training set constructed from higher angular resolution interferometric submillimeter observations.The results show that: (1) the recovery rate and precision of the developed machine-learning method are (77.2±4.7)% and (82.0±4.9)%, respectively, and the recovery rate of Submillimeter Galaxies (SMGs) reaches to 85% if we combine the radio identifications;(2)the self-test of the training set and a set of independent tests confirm the robustness of our method; (3) our analysis based on the stacked images demonstrates that the method can recover faint and/or diffuse submillimeter galaxies even if they are below the detection threshold of interferometric observations.These results mean that we have efficiently reduced the uncertainties in the follow-up studies of submillimeter sources resulting from the coarse angular resolution of single-dish submillimeter surveys.Our project is the first application of machine-learning techniques in the studies of submillimeter galaxies.We apply this method to the recent single-dish submillimeter survey in the Cosmic Evolution Survey (COSMOS) field (S2COSMOS).About 1200 optical/nearinfrared/radio counterparts of submillimeter galaxies have been identified.We study the multi-wavelength properties of submillimeter galaxies based on these precisely identified counterparts.We confirm that these identified counterparts to single-dish submillimeter sources represent the complete physical properties of submillimeter galaxies, i.e., they tend to lie at higher redshift and they are undergoing massive bursts of star formation at rates of 102M⊙·yr?1.We also apply our method to the whole COSMOS field and identify～9000 likely optical/near-infrared counterparts of submillimeter galaxies, which are below the detection limit of single-dish submillimeter observations.We can investigate the evolutionary connections between submillimeter galaxies and other populations at the same epoch or in the lower-redshift Universe by comparing their clustering strength,which reflect their underlying dark matter distributions.Our studies provide more accurate constraints on galaxies formation and evolution models, particularly for the massive ones.