一种量化天文数据模式的模拟退火算法
A Simulated Annealing algorithm to quantify patterns in astronomical data
摘要：
我们开发了一种优化算法，使用模拟退火的模式在天文数据的基础上开发的机器人视觉应用程序的技术的量化。该方法属于成本最小化算法的范畴，它是基于用户确定的相互作用-之间的模式元素-定义所寻求的结构的属性的标准。我们将该算法应用于各种各样的模拟图像，并约束自由参数; α和k，它们分别表示图像中的噪声量和算法寻找共圆结构的严格程度。我们发现，这两个参数是相互关联的，而且，独立于图案属性，对于大多数图像，适当的选择将是log（k）= -2和0 < α \lesssim 0.04。对于不同的k值，当在定义感兴趣的模式时考虑更多的相互作用系数时，有效α范围的宽度会减小。最后，我们应用该算法对N体模拟暗物质晕数据和透镜Abell 2218集群的HST图像得出结论，这种多功能的技术可以应用于结构的量化和识别天文模式的一致性。
英文摘要：
We develop an optimization algorithm, using simulated annealing for the quantification of patterns in astronomical data based on techniques developed for robotic vision applications. The methodology falls in the category of cost minimization algorithms and it is based on user-determined interaction - among the pattern elements - criteria that define the properties of the sought structures. We applied the algorithm on a large variety of mock images and we constrained the free parameters; α and k, which express the amount of noise in the image and how strictly the algorithm seeks for cocircular structures, respectively. We find that the two parameters are interrelated and also that, independently of the pattern properties, an appropriate selection for most of the images would be log(k) = -2 and 0 < α \lesssim 0.04. The width of the effective α-range, for different values of k, is reduced when more interaction coefficients are taken into account for the definition of the patterns of interest. Finally, we applied the algorithm on N-body simulation dark-matter halo data and on the HST image of the lensing Abell 2218 cluster to conclude that this versatile technique could be applied for the quantification of structure and for identifying coherence in astronomical patterns.
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