CD-HPF: New Habitability Score Via Data Analytic Modeling

TL;DR

This paper introduces a novel habitability scoring metric, the Cobb-Douglas Habitability Score (CDHS), which uses planetary parameters and advanced optimization techniques to improve classification of exoplanets' habitability.

Contribution

The paper proposes the CDHS metric based on Cobb-Douglas production functions, integrating it with machine learning for enhanced exoplanet habitability classification.

Findings

CDHS is a scalable, elastic habitability metric with analytical guarantees.

Standard PHI is a special case of CDHS.

CDHS combined with K-NN improves habitability classification accuracy.

Abstract

The search for life on the planets outside the Solar System can be broadly classified into the following: looking for Earth-like conditions or the planets similar to the Earth (Earth similarity), and looking for the possibility of life in a form known or unknown to us (habitability). The two frequently used indices, ESI and PHI, describe heuristic methods to score similarity/habitability in the efforts to categorize different exoplanets or exomoons. ESI, in particular, considers Earth as the reference frame for habitability and is a quick screening tool to categorize and measure physical similarity of any planetary body with the Earth. The PHI assesses the probability that life in some form may exist on any given world, and is based on the essential requirements of known life: a stable and protected substrate, energy, appropriate chemistry and a liquid medium. We propose here a different metric, a Cobb-Douglas Habitability Score (CDHS), based on Cobb-Douglas habitability production function (CD-HPF), which computes the habitability score by using measured and calculated planetary input parameters. The proposed metric, with exponents accounting for metric elasticity, is endowed with verifiable analytical properties that ensure global optima, and is scalable to accommodate finitely many input parameters. The model is elastic, does not suffer from curvature violations and, as we discovered, the standard PHI is a special case of CDHS. Computed CDHS scores are fed to K-NN (K-Nearest Neighbour) classification algorithm with probabilistic herding that facilitates the assignment of exoplanets to appropriate classes via supervised feature learning methods, producing granular clusters of habitability. The proposed work describes a decision-theoretical model using the power of convex optimization and algorithmic machine learning.