标题： 年轻星团的结构 显示英文标题

标题： The structure of young star clusters

摘要： 我们分析并比较了蝘蜓座I和金牛座中年轻恒星的聚类情况。 我们计算了伴星的平均面密度\bar{N}随每个恒星的角位移\theta 的变化情况。 然后我们用两个同时存在的幂律拟合\bar{N}(\theta )。 对于蝘蜓座I，幂律的指数分别为1.97和0.28，拐点约为0.011度。 对于金牛座，我们得到2.02和0.87，拐点约为0.013度。 对于这两个星团，观测数据在双星区域与最佳拟合曲线存在较大的系统性偏离。 这些偏离可能是由于最近Nakajima等人和Bate等人讨论的演化效应所致。 在聚类区域，数据与最佳拟合曲线非常吻合。 我们还计算了这两个星团的箱维数。 然而，有限的动态范围使得这些估计仅能描述大尺度的聚类，而不能作为分形性的可靠证据。 我们还提出了两种算法，用于客观生成年轻星团中恒星面密度恒定的地图。 这样的地图对于与恒星形成云的分子线图和尘埃连续谱图以及数值模拟结果进行比较是有用的。 这些地图保留了在评估\bar{N}(\theta ) 时被抑制的信息。 分析这样的地图可以用来区分分形结构和单级聚类，并确定小-N星团中的中心凝聚程度。 算法I使用一个通用的平滑长度，因此具有有限的动态范围，但它是隐式归一化的。 算法II使用局部平滑长度，因此具有更大的动态范围，但需要显式归一化。 显示英文摘要

摘要： We analyse and compare the clustering of young stars in Chamaeleon I and Taurus. We compute the mean surface-density of companion stars \bar{N} as a function of angular displacement \theta from each star. We then fit \bar{N}(\theta) with two simultaneous power laws. For Chamaeleon I, the exponents of the power laws are 1.97 and 0.28, with the elbow at ~0.011 degrees. For Taurus, we obtain 2.02 and 0.87, with the elbow at ~0.013 deg. For both star clusters the observational data make quite large systematic excursions from the best fitting curve in the binary regime. These excursions may be attributable to evolutionary effects of the types discussed recently by Nakajima et al. and Bate et al. In the clustering regime the data conform to the best fitting curve very well. We also calculate the box-dimensions for the two star clusters. However, the limited dynamic range makes these estimates simply descriptors of the large-scale clustering, and not admissible evidence for fractality. We also propose two algorithms for objectively generating maps of constant stellar surface-density in young star clusters. Such maps are useful for comparison with molecular-line and dust-continuum maps of star-forming clouds, and with the results of numerical simulations. These maps retain information which is suppressed in the evaluation of \bar{N}(\theta). Analyzing such maps can be used to discriminate between fractal structure and single-level clustering, and to determine the degree of central condensation in small-N clusters. Algorithm I uses a universal smoothing length, and therefore has a restricted dynamic range, but it is implicitly normalized. Algorithm II uses a local smoothing length, which gives it much greater dynamic range, but it has to be normalized explicitly.