Quantifying the classification of exoplanets: in search for the right habitability metric

• Published in: The European physical journal. ST, Special topics Vol. 230; no. 10; pp. 2207 - 2220
• Main Authors: Safonova, Margarita, Mathur, Archana, Basak, Suryoday, Bora, Kakoli, Agrawal, Surbhi
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2021; Springer Nature B.V
• Subjects: Artificial intelligence; Astrobiology; Astronomy; Astrophysics; Atomic; Classical and Continuum Physics; Classification; Computational geometry; Condensed Matter Physics; Convexity; Extrasolar planets; Habitability; Machine learning; Machine Learning and Astronomy; Materials Science; Measurement Science and Instrumentation; Modeling; Molecular; Neural networks; Optical and Plasma Physics; Optimization; Optimization techniques; Physics; Physics and Astronomy; Planet detection; Regular Article; Terrestrial planets
• DOI: 10.1140/epjs/s11734-021-00211-z
• ISSN: 1951-6355

What is habitability? Can we quantify it? What do we mean under the term habitable or potentially habitable planet? With estimates of the number of planets in our Galaxy alone running into billions, possibly a number greater than the number of stars, it is high time to start characterizing them, sorting them into classes/types just like stars, to better understand their formation paths, their properties and, ultimately, their ability to beget or sustain life. After all, we do have life thriving on one of these billions of planets, why not on others? Which planets are better suited for life and which ones are definitely not worth spending expensive telescope time on? We need to find sort of quick assessment score, a metric, using which we can make a list of promising planets and dedicate our efforts to them. Exoplanetary habitability is a transdisciplinary subject integrating astrophysics, astrobiology, planetary science, and even terrestrial environmental sciences. It became a challenging problem in astroinformatics, an emerging area in computational astronomy. Here, we review the existing metrics of habitability and the new classification schemes (machine learning (ML), neural networks, activation functions) of extrasolar planets, and provide an exposition of the use of computational intelligence techniques to evaluate habitability scores and to automate the process of classification of exoplanets. We examine how solving convex optimization techniques, as in computing new metrics such as Cobb–Douglas habitability score (CDHS) and constant elasticity earth similarity approach (CEESA), cross-validates ML-based classification of exoplanets. Despite the recent criticism of exoplanetary habitability ranking, we are sure that this field has to continue and evolve to use all available machinery of astroinformatics, artificial intelligence (AI) and machine learning. It might actually develop into a sort of same scale as stellar types in astronomy, to be used as a quick tool of screening exoplanets in important characteristics in search for potentially habitable planets (PHPs), or Earth-like planets, for detailed follow-up targets.