Modeling of Granulation in Red Supergiants in the Magellanic Clouds with the Gaussian Process Regressions
Zehao Zhang, Yi Ren, Biwei Jiang, Igor Soszynski, Tharindu, Jayasinghe

TL;DR
This study models the granulation in red supergiants of the Magellanic Clouds using Gaussian Process regression, establishing scaling relations for granulation parameters and analyzing the effects of metallicity.
Contribution
It introduces a novel application of Gaussian Process regression to model RSG granulation and derives new scaling relations linking granulation and stellar parameters.
Findings
Granulation amplitude and timescale increase with metallicity.
The timescale $ ho$ is nearly independent of metallicity.
Gaussian Process regression offers advantages over periodogram fitting.
Abstract
The granulation of red supergiants (RSGs) in the Magellanic Clouds are systematically investigated by combining the latest RSGs samples and light curves from the Optical Gravitational Lensing Experiment and the All-Sky Automated Survey for Supernovae. The present RSGs samples are firstly examined for foreground stars and possible misidentified sources, and the light curves are sequentially checked to remove the outliers by white noise and photometric quality. The Gaussian Process regression is used to model the granulation, and the Markov Chain Monte Carlo is applied to derive the granulation amplitude and the period of the undamped oscillator , as well as the damping timescale . The dimensionless quality factor is then calculated through . RSGs around are considered to have significant granulation signals and are used for…
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Taxonomy
TopicsStatistical and numerical algorithms · Space Exploration and Technology · Advanced Research in Science and Engineering
