Inferring Planetary Complexity using Epsilon Machines: A Novel Approach to Agnostic Biosignatures

We present a new approach to exoplanet assessment using techniques from complexity science. The majority of life detection schemes rely upon biochemical signatures and planetary context. However, it is increasingly recognized that extraterrestrial life could be very different to life on Earth. Therefore, we explore a novel, agnostic approach that focusses on temporal variability of light reflected or emitted by a planet, and the hypothesis that the presence of life correlates with complexity. 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. We show that statistical complexity is an effective measure of the complexity of planetary features. Increasing levels of qualitative planetary complexity correlate with increases in the linear sum of statistical complexity and Shannon entropy rate, demonstrating that our approach can identify planets with the richest dynamics, and possibly even the presence of life.