宇宙中冷分子气体的贝叶斯盲巡天
A Bayesian blind survey for cold molecular gas in the Universe
摘要：
提出了一种新的贝叶斯方法进行图像域搜索的线发射星系。该方法使用空间和光谱信息来鲁棒地确定源属性，采用简单的高斯模型或其他物理激励模型，同时使用证据来确定源是真实的概率。在本文中，我们描述的方法，它的应用程序都模拟的数据集，和一个盲目的调查冷分子气体的观测哈勃深场北高原德布尔干涉仪。我们在调查中总共进行了6次稳健检测，其中5次在其他观测波段有对应的检测。我们确定了在以前的调查中发现的最安全的检测，同时发现了一个新的可能的线源，其光学ID在以前的分析中没有看到。这项研究作为一个试点应用贝叶斯统计未来的搜索进行低$J$ CO转换的高红移星系使用JVLA，并在毫米波与ALMA，使强大的科学结论的推断历史的恒星形成星系在宇宙中通过宇宙时间。
英文摘要：
A new Bayesian method for performing an image domain search for line-emitting galaxies is presented. The method uses both spatial and spectral information to robustly determine the source properties, employing either simple Gaussian, or other physically motivated models whilst using the evidence to determine the probability that the source is real. In this paper, we describe the method, and its application to both a simulated data set, and a blind survey for cold molecular gas using observations of the Hubble Deep Field North taken with the Plateau de Bure Interferometer. We make a total of 6 robust detections in the survey, 5 of which have counterparts in other observing bands. We identify the most secure detections found in a previous investigation, while finding one new probable line source with an optical ID not seen in the previous analysis. This study acts as a pilot application of Bayesian statistics to future searches to be carried out both for low-$J$ CO transitions of high redshift galaxies using the JVLA, and at millimeter wavelengths with ALMA, enabling the inference of robust scientific conclusions about the history of the molecular gas properties of star-forming galaxies in the Universe through cosmic time.
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