Rapid increments in the concentration of the radiocarbon in the atmosphere (Δ14C) have been identified in the years 774-775 CE and 993-994 CE (Miyake events) using annual measurements on known-age tree-rings. The level of cosmic radiation implied by such increases could cause the failure of satellite telecommunication systems, and thus, there is a need to model and predict them. In this work, we investigated several intelligent computational methods to identify similar events in the past. We apply state-of-the-art pattern matching techniques as well as feature representation, a procedure that typically is used in machine learning and classification. To validate our findings, we used as ground truth the two confirmed Miyake events, and several other dates that have been proposed in the literature. We show that some of the methods used in this study successfully identify most of the ground truth events (~1% false positive rate at 75% true positive rate). Our results show that computational methods can be used to identify comparable patterns of interest and hence potentially uncover sudden increments of Δ14C in the past.
Keywords: COSFIRE; Digital signal processing; Feature extraction; Miyake events; Pattern matching; Radiocarbon dating.
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