An innovative tool for automating classification of stellar variability through nonlinear data analytics

Though Classical Cepheids, $\delta$-Scuti, Eclipsing Binary, Long-Period variables, and RRLyraes are abundant in most of the clusters, automating the classification of the objects faces challenges. Since the rate at which the data has been getting accumulated is enormous, this automation of classification is paramount for carrying out appropriate analysis of the objects depending on the class it belongs to. Our results prove that the proposed tool for automating stellar classification not only reduces misclassification by up to 94.79% (in case of classification between multimode subclass of $\delta$-Scuti and Mira subclass of Long-Period variables) but also improves reliability by as high as 78.35% (in case of conventionally misclassified pair of RRab subclass of RRLyrae and Fundamental Mode subclass of Classical Cepheids). Our random forest model has achieved a cross-validation accuracy of 0.88 with conventional statistical parameters coupled with tools of Nonlinear Dynamical Theory. It has achieved the highest precision and recalls for Long-Period variables of the Mira subclass (i.e., 0.99 & 0.99) and the lowest for Eclipsing Binary of subclass contact (i.e., 0.81 & 0.77). A positive improvement in accuracy rate by 7.3% is observed when compared with a model based on a conventional statistical platform. This proves the significance of introducing the proposed tools in devising an automated classification model for stellar variables.