Estimating activity cycles with probabilistic methods II. The Mount Wilson Ca H&K data
N. Olspert, J. Lehtinen, M. J. K\"apyl\"a, J. Pelt, A. Grigorievskiy

TL;DR
This study applies advanced Gaussian process models to analyze stellar activity cycles using Mount Wilson Ca H&K data, revealing insights into cycle populations, trends, and the impact of modeling assumptions on cycle detection.
Contribution
It introduces novel Gaussian process-based methods for analyzing stellar activity cycles and demonstrates how modeling choices influence cycle detection and interpretation.
Findings
Confirmed two populations in activity-period diagram
Found cycle periods shorten with increasing rotation in inactive stars
Indicated a smooth operation of stellar dynamos across activity levels
Abstract
Debate over the existence of branches in the stellar activity-rotation diagrams continues. Application of modern time series analysis tools to study the mean cycle periods in chromospheric activity index is lacking. We develop such models, based on Gaussian processes, for one-dimensional time series and apply it to the extended Mount Wilson Ca H&K sample. Our main aim is to study how the previously commonly used assumption of strict harmonicity of the stellar cycles as well as handling of the linear trends affects the results. We introduce three methods of different complexity, starting with the simple Bayesian harmonic model and followed by Gaussian Process models with periodic and quasi-periodic covariance functions. We confirm the existence of two populations in the activity-period diagram. We find only one significant trend in the inactive population, namely that the cycle periods…
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Taxonomy
MethodsGaussian Process
