Assessing Planetary Complexity and Potential Agnostic Biosignatures using Epsilon Machines

TL;DR

This paper introduces an agnostic, complexity-based method using epsilon machine reconstruction to analyze exoplanet light variability, aiming to identify biosignatures through planetary dynamics without relying on biochemical markers.

Contribution

The study applies complexity science to exoplanet data, demonstrating that statistical complexity correlates with planetary features and can distinguish Earth-like planets from gas giants.

Findings

Earth exhibits higher complexity and entropy than Jupiter across studied wavelengths.

Statistical complexity effectively measures planetary feature richness.

The approach offers a biosignature detection method independent of biochemical assumptions.

Abstract

We present a new approach to exoplanet characterisation 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 recognised that extraterrestrial life could be very different to 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.