Astroinformatics is a new impact area in the world of astronomy, occasionally called the final frontier, where several astrophysicists, statisticians and computer scientists work together to tackle various data intensive astronomical problems. Exponential growth in the data volume and increased complexity of the data augments difficult questions to the existing challenges. Classical problems in Astronomy are compounded by accumulation of astronomical volume of complex data, rendering the task of classification and interpretation incredibly laborious. The presence of noise in the data makes analysis and interpretation even more arduous. Machine learning algorithms and data analytic techniques provide the right platform for the challenges posed by these problems. A diverse range of open problem like star-galaxy separation, detection and classification of exoplanets, classification of supernovae is discussed. The focus of the paper is the applicability and efficacy of various machine learning algorithms like K Nearest Neighbor (KNN), random forest (RF), decision tree (DT), Support Vector Machine (SVM), Na\”ive Bayes and Linear Discriminant Analysis (LDA) in analysis and inference of the decision theoretic problems in Astronomy. The machine learning algorithms, integrated into ASTROMLSKIT, a toolkit developed in the course of the work, have been used to analyze HabCat data and supernovae data. Accuracy has been found to be appreciably good.
S. Saha, S. Agrawal, M. R, et. al.
Thu, 30 Apr 15
Comments: Habitability Catalog (HabCat), Supernova classification, data analysis, Astroinformatics, Machine learning, ASTROMLS toolkit, Na\”ive Bayes, SVD, PCA, Random Forest, SVM, Decision Tree, LDA