Hybrid -- To Join via Zoom
In this talk, I will present a new approach to exoplanet characterisation that uses techniques from complexity science. This agnostic method makes use of the temporal variability of light reflected or emitted from a planet. We use a technique known as epsilon machine reconstruction to compute the statistical complexity, a measure of the minimal model size for time series data, and demonstrate that statistical complexity is an effective measure of the complexity of planetary features. Increasing levels of qualitative planetary complexity correlate with increases in statistical complexity and Shannon entropy, demonstrating that our approach can identify planets with the richest dynamics. We also compared Earth time series with Jupiter data, and found that for the three wavelengths considered, Earth's average complexity and entropy rate are approximately 50% and 43% higher than Jupiter's, respectively. Under the hypothesis that there is a correlation between the presence of a biosphere and observable planetary complexity, our technique offers an agnostic and quantitative method for the measurement thereof.