CD-HPF: New habitability score via data analytic modeling

Bora, Kakoli; Saha, Snehanshu; Agrawal, Sachin; Safonova, Margarita; Routh, Swati; Narasimhamurthy, Anand

doi:10.1016/j.ascom.2016.08.001

Cited by 54 publications

(54 citation statements)

References 18 publications

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“…The CDHS values in CRS and DRS cases were 1.083 and 1.095, respectively. The degree/extent of closeness is explained in (Bora et al, 2016) in great detail.…”

Section: Cdhs Calculation Using Radius Density Escape Velocity and mentioning

confidence: 99%

“…Proxima b passed the scrutiny and is classified as a member of the "Earth League". (Bora et al, 2016) used a library function fmincon to compute the elasticity values. Here, we have implemented a more efficient algorithm to perform the same task.…”

Section: Cdhs Calculation Using Stellar Flux and Massmentioning

confidence: 99%

“…where x n is an initial value of x, x n+1 the new value of x, ∂F ∂x is the slope of function Y = F (x) and χ denotes the step size, which is greater than 0 and forces the algorithm to make a small jump (descent or ascent algorithms are trained to make small jumps in the direction of the new update). Stochastic variant thus mitigates the oscillating nature of the global optima -a frequent malaise in the conventional Gradient Ascent/Descent and Newton-like methods, such as fmincon used in (Bora et al, 2016). At this point of time, without further evidence of recorded/measured parameters, it may not be prudent to scale up the CD-HPF model by including more parameters other than the ones used by either ESI or our model.…”

Section: Computing Elasticities Via Gradient Ascentmentioning

confidence: 99%

“…The traditional active-set method is divided into two steps, focusing on feasibility and optimality, in that order. Instead of an "ad-hoc" start, active set methods bank on a good "initiator" estimate of the optimal (Bora et al, 2016).…”

Section: Using Active Setmentioning

confidence: 99%

“…With discoveries of exoplanets pouring in hundreds, it is becoming necessary to develop some sort of a quick screening tool -a ranking scale -for evaluating habitability perspectives for the follow-up targets. We have proposed a novel inductive approach, inspired by the Cobb-Douglas model from production economics, to verify theoretical conditions of global optima of the functional form to model and to compute the habitability score of exoplanets -the Cobb-Douglas Habitability Score (CDHS) (Bora et al, 2016). While our paper "CD-HPF: New Habitability Score Via Data Analytic Modeling" was in production, the discovery of an exoplanet Proxima b orbiting the nearest star (Proxima Centauri) to the Sun was announced (Angala, Figure 1: The convergence of two different approaches in the investigation of potential habitability.…”

Section: Introductionmentioning

confidence: 99%

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Theoretical validation of potential habitability via analytical and boosted tree methods: An optimistic study on recently discovered exoplanets

Saha

Basak

Safonova³

et al. 2018

Astronomy and Computing

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Seven Earth-sized planets, known as the TRAPPIST-1 system was discovered with great fanfare in the last week of February 2017. Three of these planets are in the habitable zone of their star, making them potentially habitable planets a mere 40 light years away. Discovery of the closest potentially habitable planet to us just a year before -Proxima b and a realization that Earth-type planets in circumstellar habitable zones are a common occurrence provides the impetus to the existing pursuit for life outside the Solar System. The search for life has two goals essentially: Earth similarity and habitability. An index was recently proposed, Cobb-Douglas Habitability Score (CDHS), based on Cobb-Douglas habitability production function, which computes the habitability score by using measured and estimated planetary parameters like radius, density, escape velocity and surface temperature of a planet. The proposed metric, with exponents accounting for metric elasticity, is endowed with analytical properties that ensure global optima and can be scaled to accommodate a finite number of input parameters. We show here that the model is elastic, and the conditions on elasticity to ensure global maxima can scale as the number of predictor parameters increase. K-Nearest Neighbor classification algorithm, embellished with probabilistic herding and thresholding restriction, utilizes CDHS scores and labels exoplanets to appropriate classes via feature-learning methods. The algorithm works on top of a decision-theoretical model using the power of convex optimization and machine learning. The goal is to classify the recently discovered exoplanets into the "Earth League" and other classes. A second approach, based on a novel feature-learning and tree-building method classifies the same planets without computing the CDHS of the planets and produces a similar outcome. The convergence of the two different approaches indicates the strength of the proposed scheme and the likelihood of the potential habitability of the recent discoveries. 2016). This planet generated a lot of stir in the news (Witze, 2016) because it is located in the habitable zone and its mass is in the Earth's mass range: 1.27 − 3 M ⊕ , making it a potentially habitable planet (PHP) and an immediate destination for the Breakthrough Starshot initiative (Starshot, 2016). A few months after the announcement of Proxima b, another family of terrestrial-size exoplanets -the TRAPPIST-1 systemwas discovered (Gillon, 2016).This work is motivated by testing the efficacy of the suggested model, CDHS, in determining the habitability score, the proximity to the "Earth-League", of the recently discovered Proxima b. The habitability score model has been found to work well in classifying previously known exoplanets in terms of potential habitability. Therefore it was natural to test whether the model can also classify it as potentially habitable by computing its habitability score. This could indicate whether the model may be extended for a quick check of the potential habitability of n...

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“…The CDHS values in CRS and DRS cases were 1.083 and 1.095, respectively. The degree/extent of closeness is explained in (Bora et al, 2016) in great detail.…”

Section: Cdhs Calculation Using Radius Density Escape Velocity and mentioning

confidence: 99%

Section: Cdhs Calculation Using Stellar Flux and Massmentioning

confidence: 99%

Section: Computing Elasticities Via Gradient Ascentmentioning

confidence: 99%

Section: Using Active Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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