Postulating Exoplanetary Habitability via a Novel Anomaly Detection Method
Jyotirmoy Sarkar, Kartik Bhatia, Snehanshu Saha, Margarita Safonova, and Santonu Sarkar

TL;DR
This paper introduces a novel anomaly detection method, MSMA, to identify potentially habitable exoplanets by treating Earth as an anomaly, offering a new approach beyond traditional classification methods.
Contribution
It proposes the MSMA and MSMVMCA algorithms for anomaly detection in exoplanet data, providing a new perspective on habitability assessment.
Findings
MSMA effectively detects habitable exoplanet candidates as anomalies.
The approach aligns well with existing habitable exoplanet catalogs.
It offers an alternative to supervised classification methods.
Abstract
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…
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