密集场天体测量与空间干涉测量任务- I。由混淆引起的单次测量天体测量偏差的估计
Crowded-Field Astrometry with the Space Interferometry Mission - I. Estimating the Single-Measurement Astrometric Bias Arising from Confusion
摘要：
未来空间干涉测量任务（SIM）对恒星目标的位置测量精度不仅受到光子噪声和仪器性能（设计、稳定性等）的限制。以及总体测量方案（观察策略、还原方法等），而且还受到视场（FOV）中其他“令人困惑的”恒星的存在的影响。我们使用一个简单的“相量”模型作为一种援助，以了解这种“混淆偏差”的主要影响，在单一的观察SIM。这一分析模型已在计算机代码中进行了数值计算，并应用于从已接受的一些关键项目中选择的典型SIM目标场。我们预计，在所有SIM目标中，只有不到1%的目标容易受到混淆偏差的影响;我们表明，对于目前的SIM设计，如果场星的表面密度超过0.4星/弧秒^[2]，混淆可能是一个问题。我们已经开发了一个软件工具，作为一个援助，以确定可能存在的混淆偏见，在任何任意领域的单一观察。视场中少数最亮恒星的位置和光谱能量分布的一些先验知识有助于确定可能存在的混淆偏差，但这些信息通常不太可能具有足够的精度以允许其消除。我们讨论了几种方法，减少混乱的偏见在拥挤的领域的可能性。最后，本半解析方法的几个局限性进行了审查，并估计其对目前的结果的影响。这里提出的简单模型提供了一个很好的物理理解如何混淆出现在一个单一的SIM观察，并有足够的精度，以建立在大多数情况下的偏差的可能性。
英文摘要：
The accuracy of position measurements on stellar targets with the future Space Interferometry Mission (SIM) will be limited not only by photon noise and by the properties of the instrument (design, stability, etc.) and the overall measurement program (observing strategy, reduction methods, etc.), but also by the presence of other "confusing" stars in the field of view (FOV). We use a simple "phasor" model as an aid to understanding the main effects of this "confusion bias" in single observations with SIM. This analytic model has been implemented numerically in a computer code and applied to a selection of typical SIM target fields drawn from some of the Key Projects already accepted for the Mission. We expect that less than 1% of all SIM targets will be vulnerable to confusion bias; we show that for the present SIM design, confusion may be a concern if the surface density of field stars exceeds 0.4 star/arcsec^2. We have developed a software tool as an aid to ascertaining the possible presence of confusion bias in single observations of any arbitrary field. Some a priori knowledge of the locations and spectral energy distributions of the few brightest stars in the FOV is helpful in establishing the possible presence of confusion bias, but the information is in general not likely to be available with sufficient accuracy to permit its removal. We discuss several ways of reducing the likelihood of confusion bias in crowded fields. Finally, several limitations of the present semi-analytic approach are reviewed, and their effects on the present results are estimated. The simple model presented here provides a good physical understanding of how confusion arises in a single SIM observation, and has sufficient precision to establish the likelihood of a bias in most cases.
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