Calibrated radiocarbon (14C) determinations are commonly used in archaeology to assign calendar dates to a site’s chronological phases identified based on additional evidence such as stratigraphy. In the absence of such evidence, we can perform dense 14C sampling of the site to attempt to identify periods of heightened activity, separated by periods of inactivity, which correspond to archaeological phases and gaps between them. We propose a method to achieve this by hierarchical cluster analysis of the calibrated 14C dates, followed by testing of the different clustering solutions for consistency based on silhouette coefficient and statistical significance using randomization. Separate events identified in such a way can then be regarded as evidence for distinct phases of activity and used to construct a site-specific sequence. This can be in turn used as a Bayesian prior to further narrow down the distributions of the calibrated 14C dates. We assessed the validity of the method using simulated data as well as real-life archaeological data from the Bronze Age settlement of Troy. A Python implementation of the method is available online at https://github.com/demjanp/clustering_14C.

中文翻译：

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校准的放射性碳日期的聚类：由密集放射性碳采样确定的特定地点的年代序列

校准放射性碳 (14C) 考古学中通常使用确定来将日历日期分配给基于附加证据（例如地层学）确定的站点的时间阶段。在没有这样的证据的情况下，我们可以进行密集14C 对场地进行采样，以尝试识别活动增加的时期，由不活动的时期分隔，这与考古阶段和它们之间的间隙相对应。我们提出了一种通过校准的层次聚类分析来实现这一点的方法14C 日期，然后使用随机化基于轮廓系数和统计显着性测试不同聚类解决方案的一致性。以这种方式识别的单独事件可以被视为活动不同阶段的证据，并用于构建特定地点的序列。在进一步缩小校准分布之前，这可以反过来用作贝叶斯14C日期。我们使用模拟数据以及来自特洛伊青铜时代定居点的真实考古数据评估了该方法的有效性。该方法的 Python 实现可在线获取https://github.com/demjanp/clustering_14C.