CD-HPF：通过数据分析建模的新可居住性评分
CD-HPF: New Habitability Score Via Data Analytic Modeling
摘要：
在太阳系外的行星上寻找生命大致可以分为以下几种：寻找类似地球的条件或与地球相似的行星（地球相似性），以及寻找我们已知或未知形式的生命的可能性（可居住性）。两个经常使用的指数，ESI和PHI，描述了启发式方法来评分相似性/可居住性，以分类不同的系外行星或系外卫星。特别是，ESI将地球视为可居住性的参考系，是一种快速筛选工具，用于分类和测量任何行星体与地球的物理相似性。潜在生命指数评估了某种形式的生命可能存在于任何给定世界的概率，并基于已知生命的基本要求：稳定和受保护的基质，能量，适当的化学物质和液体介质。我们在这里提出了一个不同的度量，Cobb-Douglas适居性得分（CDHS），基于Cobb-Douglas适居性生产函数（CD-HPF），它通过使用测量和计算的行星输入参数来计算适居性得分。建议的度量，指数占度量弹性，被赋予可验证的分析属性，确保全局最优，并可扩展到容纳100多个输入参数。该模型是弹性的，不受曲率违规，正如我们发现的，标准的PHI是一个特殊的情况下，CDHS。计算的CDHS分数被馈送到具有概率放牧的K-NN（K最近邻）分类算法，该算法通过监督特征学习方法将系外行星分配到适当的类别，产生可居住性的颗粒集群。所提出的工作描述了一个决策理论模型，使用凸优化和算法机器学习的力量。
英文摘要：
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.
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