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The search for life on the planets outside the Solar System can be broadlyclassified into the following: looking for Earth-like conditions or the planetssimilar to the Earth (Earth similarity), and looking for the possibility oflife in a form known or unknown to us (habitability). The two frequently usedindices, ESI and PHI, describe heuristic methods to scoresimilarity/habitability in the efforts to categorize different exoplanets orexomoons. ESI, in particular, considers Earth as the reference frame forhabitability and is a quick screening tool to categorize and measure physicalsimilarity of any planetary body with the Earth. The PHI assesses theprobability that life in some form may exist on any given world, and is basedon the essential requirements of known life: a stable and protected substrate,energy, appropriate chemistry and a liquid medium. We propose here a differentmetric, a Cobb-Douglas Habitability Score (CDHS), based on Cobb-Douglashabitability production function (CD-HPF), which computes the habitabilityscore by using measured and calculated planetary input parameters. The proposedmetric, with exponents accounting for metric elasticity, is endowed withverifiable analytical properties that ensure global optima, and is scalable toaccommodate finitely many input parameters. The model is elastic, does notsuffer from curvature violations and, as we discovered, the standard PHI is aspecial case of CDHS. Computed CDHS scores are fed to K-NN (K-NearestNeighbour) classification algorithm with probabilistic herding that facilitatesthe assignment of exoplanets to appropriate classes via supervised featurelearning methods, producing granular clusters of habitability. The proposedwork describes a decision-theoretical model using the power of convexoptimization and algorithmic machine learning.

CD-HPF: New Habitability Score Via Data Analytic Modeling