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arXiv 1102.1752astro-ph.CO
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多视线间Lyman-alpha森林相关性的估计

On Estimating Lyman-alpha Forest Correlations between Multiple Sightlines

Matthew McQuinn, Martin White

摘要:

莱曼-阿尔法森林研究的下一个前沿是从密集的背景源样本中重建3D相关性。三维相关性的测量有可能改善对基本宇宙学参数、电离背景模型和再电离历史的约束。这项研究解决了光谱调查的灵敏度在莱曼α森林的3D相关性。我们表明,调查的灵敏度,这个信号可以量化的只是一个单一的数字,一个噪声加权的数密度的来源在天空中。我们研究了光谱类星体(或星系)调查的灵敏度如何作为其深度,面积和红移的函数。我们提出了一个简单的方法加权视线与不同的S/N水平估计的关联函数,我们表明,这种估计一般执行以及最小方差二次估计。此外,我们还指出,如果在一个L * 类星体上观测每个场的时间足够长,在一个1 A像素内达到S/N ~ 2,同时获得所有L > L * 类星体的光谱,则观测对通量关联函数的灵敏度一般是最大的:在同一目标上积分时间越长或包括较暗的类星体,获得的结果就越少。我们量化这些考虑因素如何与角直径距离,时空曲率和再电离历史的约束。

英文摘要:

The next frontier of Lyman-alpha forest studies is the reconstruction of 3D correlations from a dense sample of background sources. The measurement of 3D correlations has the potential to improve constraints on fundamental cosmological parameters, ionizing background models, and the reionization history. This study addresses the sensitivity of spectroscopic surveys to 3D correlations in the Lyman-alpha forest. We show that the sensitivity of a survey to this signal can be quantified by just a single number, a noise-weighted number density of sources on the sky. We investigate how the sensitivity of a spectroscopic quasar (or galaxy) survey scales as a function of its depth, area, and redshift. We propose a simple method for weighting sightlines with varying S/N levels to estimate the correlation function, and we show that this estimator generally performs nearly as well as the minimum variance quadratic estimator. In addition, we show that the sensitivity of a quasar survey to the flux correlation function is generally maximized if it observes each field just long enough to achieve S/N ~ 2 in a 1 A pixel on an L_* quasar while acquiring spectra for all quasars with L > L_*: Little is gained by integrating longer on the same targets or by including fainter quasars. We quantify how these considerations relate to constraints on the angular diameter distance, the curvature of space-time, and the reionization history.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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