CEESA meets machine learning: A Constant Elasticity Earth Similarity Approach to habitability and classification of exoplanets

Basak, Suryoday; Saha, Snehanshu; Mathur, Archana; Bora, Kakoli; Makhija, Simran; Safonova, Margarita; Agrawal, Sachin

doi:10.1016/j.ascom.2019.100335

Cited by 14 publications

(9 citation statements)

References 8 publications

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“…Detecting potentially habitable exoplanets using machine learning tools have gained momentum in recent times (Bora et al 2016;Saha et al 2018;Basak et al 2020). This research suggests that the habitability can be viewed as probabilistic measure (Bora et al 2016), and such approaches require optimization and classification methods.…”

Section: Habitability Quantification and Classification: Existing App...mentioning

confidence: 99%

“…Bora et al ( 2016) introduced a Cobb-Douglas Habitability Score -a metric based on Cobb-Douglas habitability production function (CD-HPF), which computes the habitability score by using measured and estimated planetary parameters. Basak et al (2020) extended the CDHS model and proposed another quantitative metric for habitability -CEESA, which considers orbital eccentricity in addition to the same features used by the CDHS. These metrics, based on optimization methods, use only four physical planetary parameters (mass, density, radius and surface temperature), while there may be a need to accommodate more features such as, for ex., eccentricity, or orbital separation.…”

Section: Metric-based Quantificationmentioning

confidence: 99%

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Postulating Exoplanetary Habitability via a Novel Anomaly Detection Method

Sarkar,

Bhatia,

Saha

et al. 2021

Preprint

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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.

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Section: Habitability Quantification and Classification: Existing App...mentioning

confidence: 99%

Section: Metric-based Quantificationmentioning

confidence: 99%

Postulating Exoplanetary Habitability via a Novel Anomaly Detection Method

Sarkar,

Bhatia,

Saha

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Since these eccentricity values are not beyond reasonable values, such models (CDHS) are therefore not tenable for accommodating the eccentricity parameter. To mitigate this problem, our group developed a new habitability metric, the Constant Elasticity Earth Similarity Approach (CEESA) [68]. The proposed metric incorporates eccentricity as one of the component features for estimation of the potential habitability of extrasolar planets.…”

Section: Constant Elasticity Earth Similarity Approach (Ceesa)mentioning

confidence: 99%

“…A relationship between the two habitability metrics, CEESA and CDHS, was derived in [68]. The general form of the Constant Elasticity of Substitution (CES) production function [69] for two inputs (say, radius R and density D) is…”

Section: Ceesa and Cdhs -The Relationshipmentioning

confidence: 99%

Quantifying the Classification of Exoplanets: in Search for the Right Habitability Metric

Safonova,

Mathur,

Basak

et al. 2021

Preprint

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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, 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 a CDHS and b 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.

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“…In the last five years more studies have continued developing new indexes such as the Mars Similarity Index, MSI, focused on identifying planets that might be habitable but under extreme conditions of life ; , the Cobb-Douglas Habitability Score (CDHS) based on a production function and physical parameters such as radio, density, escape velocity and surface temperature (Bora et al, 2016) which has been recently recomputed (Basak et al, 2020) with the imputed eccentricity values by the 'Constant Elasticity Earth Similarity Approach (CEESA), or the Statistical-likelihood Exo-Planetary Habitability Index (SEPHI) which applies likelihood functions to estimate the habitability potential using seven inputs of both planets and their stars and condensing all this information in four sub-indexes (Rodríguez-Mozos & Moya, 2017). In this work, the selected inputs are related to the main physical information the planetary discovery projects provided.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy Multi-Criteria Decision Making approach for Exo-Planetary Habitability

Sánchez-Lozano

Moya

Rodríguez-Mozos

2021

Astronomy and Computing

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Nowadays, we know thousands of exoplanets, some of them potentially habitable. Next technological facilities (JWST, for example) have exoplanet atmosphere analysis capabilities, but they also have limits in terms of how many targets can be studied.Therefore, there is a need to rank and prioritize these exoplanets with the aim of searching for biomarkers. Some criteria involved, such as the habitability potential of a dry-rock planet versus a water-rich planet, or a potentially-locked planet versus a tidally-locked planet, are often vague and the use of the fuzzy set theory is advisable.We have applied a combination of Multi-Criteria Decision-Making methodologies with fuzzy logic, the Fuzzy Reference Ideal Method (FRIM), to this problem. We have analyzed the habitability potential of 1798 exoplanets from TEPCat database based on a set of criteria (composition, atmosphere, energy, tidal locking, type of planet and liquid water), in terms of their similarity to the only ideal alternative, The Earth. Our results, when compared with the probability index SEPHI, indicate that Kepler-442b, Kepler-062e/f, and LHS_1140b are the best exoplanets for searching for biomarkers, regardless its technical difficulty. If we take into account current technical feasibility, the best candidate is TRAPPIST-1e.

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CEESA meets machine learning: A Constant Elasticity Earth Similarity Approach to habitability and classification of exoplanets

Cited by 14 publications

References 8 publications

Postulating Exoplanetary Habitability via a Novel Anomaly Detection Method

Postulating Exoplanetary Habitability via a Novel Anomaly Detection Method

Quantifying the Classification of Exoplanets: in Search for the Right Habitability Metric

A fuzzy Multi-Criteria Decision Making approach for Exo-Planetary Habitability

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