A profound shift in the study of cosmology came with the discovery of thousands of exoplanets and the possibility of the existence of billions of them in our Galaxy. The biggest goal in these searches is whether there are other life-harbouring planets. However, the question which of these detected planets are habitable, potentially-habitable, or maybe even inhabited, is still not answered. Some potentially habitable exoplanets have been hypothesized, but since Earth is the only known habitable planet, measures of habitability are necessarily determined with Earth as the reference. Several recent works introduced new habitability metrics based on optimization methods. Classification of potentially habitable exoplanets using supervised learning is another emerging area of study. However, both modeling and supervised learning approaches suffer from drawbacks. We propose an anomaly detection method, the Multi-Stage Memetic Algorithm (MSMA), to detect anomalies and extend it to an unsupervised clustering algorithm MSMVMCA to use it to detect potentially habitable exoplanets as anomalies. The algorithm is based on the postulate that Earth is an anomaly, with the possibility of existence of few other anomalies among thousands of data points. We describe an MSMA-based clustering approach with a novel distance function to detect habitable candidates as anomalies (including Earth). The results are cross-matched with the habitable exoplanet catalog (PHL-HEC) of the Planetary Habitability Laboratory (PHL) with both optimistic and conservative lists of potentially habitable exoplanets.
翻译:宇宙学研究发生了深刻的变化,发现了成千上万的外行星,并有可能在银河系中存在数十亿的外行星。这些搜索的最大目标是,是否还有其他有生命危险的行星。然而,这些被探测到的行星中哪些行星是可居住、可居住或甚至有人居住的问题仍然没有得到解答。一些可能居住的外行星是虚小的,但是由于地球是唯一已知的可居住行星,因此必须用地球来确定可居住性测量标准。最近的一些工程采用了基于优化方法的新的可居住性测量标准。利用受监督的学习对潜在可居住外行星进行分类是另一个新出现的研究领域。然而,建模和受监督的学习方法都有倒退之处。我们提出了一种异常检测方法,即多层测量高光学高光谱(MSMA),以探测异常现象,将其扩展至一个不超强的组合算法(MSMMMMMCA),以便用它来检测可居住的可居住性直流数据作为异常。根据后地球的可使用算法进行可居住性测量性测量的可居住性测量性外行星测量数据。我们以可使用可移动的可移动的离式模型为一种反常态模型,其中的超常态数据,其中的超常态数据是具有可移动的超常态的超常态的超常态,而有可移动的超常态数据。