Abstract Details
| Name: Margarita Safonova Affiliation: Indian Institute of Astrophysics Conference ID: ASI2018_684 Title : Pros and Cons of Classification of Exoplanets: in Search for the Right Habitability Metric Authors and Co-Authors : Snehanshu Saha (Department of Computer Science and Engineering, PESIT-BSC, Bangalore). Jayant Murthy (Indian Institute of Astrophysics, Bangalore), Madhu Kashyap (Jyoti Nivas College, Bangalore), Surbhi Agrawal (Department of Computer Science and Engineering, PESIT-BSC, Bangalore), Suryoday Basak (Department of Computer Science and Engineering, PESIT-BSC, Bangalore), Swati Routh (Department of Physics, Jain University, Bangalore), Kakoli Bora (Department of Information Science and Engineering, PESIT-BSC, Bangalore), Anand Narasimhamurthy (BITS, Hyderabad) Abstract Type : Poster Abstract Category : Stars,ISM and the Galaxy Abstract : Humanity always looking for second Earth since anthropically we believe that life can only originate and exist on planets, therefore the most fundamental interest is in finding the Earth twin.This can be broadly classified into looking for planets similar to the Earth (Earth similarity) or looking for the possibility of life (habitability).But is habitability the ability of a planet to beget life, or is it our ability to detect it: a planet may host life as we know it (be not just habitable but inhabited),but we will not detect it unless it evolved sufficiently to change environment on a planetary scale.Full assessment of any planet habitability requires very detailed information about it.With thousands of discovered exoplanets and possibility that stars with planets are a rule rather than an exception,it became necessary to prioritise the planets to look at, develop some sort of a quick screening tool for evaluating habitability perspectives from observed properties. Several scales were introduced:Earth Similarity Index,with Earth the reference frame for habitability;Planetary Habitability Index, based on general requirements of life like water or substrate,etc.We introduced Mars Similarity Index,as well as novel machine-learning-based classification:Cobb-Douglas Habitability Score.We perceive habitability as a probabilistic measure,or a measure with varying degrees of certainty;in contrast to the binary definition of exoplanets being “habitable or non-habitable". The approach requires classification methods that are part of ML techniques and convex optimization.However,this classification strategy has caveats,and some authors reject it entirely on the basis of impossibility to quantitatively compare habitability,and the idea that pretending otherwise can risk damaging the field in the eyes of the community. In addition, ESI is based on the well-known statistical Bray-Curtis scale of quantifying the difference between samples, frequently used by ecologists to quantify differences between samples based on count data. However, most multivariate community analyses are about understanding a complex dataset and not finding the "truth", meant in a sense of "significance". Thus, it may not be enough to understand a complex hierarchy of classification. But since all we know is Earth-based habitability, our search for habitable exoplanets (Earth-like life clearly favoured by Earth-like conditions) is by necessity anthropocentric, and any such indexing has to be centred around finding Earth-like planets, at least initially. We discuss different `habitability’ classification metrics,their origins, merits and drawbacks.Despite recent criticism of exoplanetary ranking, we are sure that this field has to continue and evolve to use all available machinery of astroinformatics and ML. It might actually develop into a sort of same scale as stellar types in astronomy. It can be used as a quick tool of screening planets in important characteristics in search for Earth-likeness for follow-up targets. |