A tool for the ages: The Probabilistic Cosmogenic Age Analysis Tool (P-CAAT)

Abstract

While revolutionary to the geomorphic community, the application of terrestrial cosmogenic nuclide (TCN) dating is complicated by geological uncertainties, which often lead to skewed or poorly clustered TCN age distributions. Although a range of statistical approaches are typically used to detect and remove outliers, few are optimized for analysis of TCN datasets. Many are mean- or median-based and therefore explicitly assume a single probability distribution (e.g., Mean Squared Weighted Deviates, Chauvenet's Criterion, etc.). Given the ubiquity of pre- and post-depositional modification of rock surfaces, which occur at different rates in different geomorphic settings, these approaches struggle with multimodal distributions which often characterize TCN datasets. In addition, most statistical approaches do not propagate measurement or production rate uncertainties, which become increasingly important as dataset size or clustering increases. Finally, most approaches provide arithmetic single solutions, irrespective of geologic context. To address these limitations, we present the Probabilistic Cosmogenic Age Analysis Tool (P-CAAT), a new approach for outlier detection and landform age analysis. This tool incorporates both sample age and geologic uncertainties and uses Monte Carlo simulations to eliminate dataset skewness by isolating component normal distributions from a cumulative probability density estimate for datasets with three or more samples. This approach allows geologic context to inform post-analysis interpretations, as researchers can assign landform ages based upon statistically distinct subpopulations, informed by the characteristics of geomorphic systems (e.g., exhumation of boulders as moraines degrade through time). To evaluate the effectiveness of P-CAAT, we analyzed a range of synthetic TCN datasets and compared the results to commonly used statistical approaches for outlier detection. Irrespective of dataset size or clustering, P-CAAT outperformed other approaches and returned accurate solutions that improve in precision as sample size increases. To enable more comprehensive utilization of our approach, P-CAAT is packaged with a GUI interface and is available for download at kgs. uky.edu/anorthite/PCAAT.