论文标题
多对象光谱调查的概率纤维到目标分配算法
Probabilistic fibre-to-target assignment algorithm for multi-object spectroscopic surveys
论文作者
论文摘要
语境。目前计划或正在建设几个新的多物体光谱仪,可以同时观察成千上万的银河系和外层面物体。 目标。在本文中,我们提出了一种概率的纤维到靶标分配算法,该算法将靶向限制的靶向量尺寸考虑在内,并且能够处理多个并发调查。我们使用4米多对象光谱望远镜(最4个)表示该算法。 方法。提出的算法的关键思想是将概率分配给纤维 - 靶向对。概率的分配将光纤定位器的功能和约束考虑在内。此外,这些概率包括调查的要求,并考虑到每个调查中目标的曝光时间,数量密度变化以及目标的角度聚类。概率方法的主要优点是,它允许对不同调查的目标选择函数进行准确而轻松的计算,这涉及确定在给定输入目录的情况下确定观察目标的概率。 结果。概率纤维到靶标分配使我们能够在单个视野中实现最大均匀的完整性。所提出的算法最大化成功观察到的目标的分数,同时最大程度地将选择偏差作为暴露时间的函数。在几项并发调查的情况下,该算法最大程度地满足了每个调查的科学要求,并且没有对特定的调查受到惩罚或优先。 结论。提出的算法是针对4个项目的建议解决方案,该解决方案允许对许多同时调查进行公正的靶向。通过一些修改,该算法也可以应用于其他多对象光谱调查。
Context. Several new multi-object spectrographs are currently planned or under construction that are capable of observing thousands of Galactic and extragalactic objects simultaneously. Aims. In this paper we present a probabilistic fibre-to-target assignment algorithm that takes spectrograph targeting constraints into account and is capable of dealing with multiple concurrent surveys. We present this algorithm using the 4-metre Multi-Object Spectroscopic Telescope (4MOST) as an example. Methods. The key idea of the proposed algorithm is to assign probabilities to fibre-target pairs. The assignment of probabilities takes the fibre positioner's capabilities and constraints into account. Additionally, these probabilities include requirements from surveys and take the required exposure time, number density variation, and angular clustering of targets across each survey into account. The main advantage of a probabilistic approach is that it allows for accurate and easy computation of the target selection function for the different surveys, which involves determining the probability of observing a target, given an input catalogue. Results. The probabilistic fibre-to-target assignment allows us to achieve maximally uniform completeness within a single field of view. The proposed algorithm maximises the fraction of successfully observed targets whilst minimising the selection bias as a function of exposure time. In the case of several concurrent surveys, the algorithm maximally satisfies the scientific requirements of each survey and no specific survey is penalised or prioritised. Conclusions. The algorithm presented is a proposed solution for the 4MOST project that allows for an unbiased targeting of many simultaneous surveys. With some modifications, the algorithm may also be applied to other multi-object spectroscopic surveys.