Assessing planetary complexity and potential agnostic biosignatures using epsilon machines

We present a new approach to exoplanet characterization using techniques from complexity science, with potential applications to biosignature detection. 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. We 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 compare Earth time series with Jupiter data, and find that for the three wavelengths considered Earth's average complexity and entropy rate are approximately 50% and 43% higher than Jupiter's, respectively. The majority of schemes for the detection of extraterrestrial life rely upon biochemical signatures and planetary context. However, it is increasingly recognized that extraterrestrial life could be very different from life on Earth. 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. A method to measure the complexity of planetary signals from remote-sensing observations is presented. Tests using Earth observations point to a correlation between increasing complexity and the presence of an extensive biosphere, showing the potential of this agnostic method to identify promising targets for life.

Automated summary

This article presents a new approach to exoplanet characterization using complexity science techniques, which can evaluate the complexity of planets by analyzing the temporal variability of light. The method is demonstrated by comparing Earth and Jupiter data, showing its potential in identifying planets with rich dynamics.