2023MNRAS.525.5789K

We used machine learning techniques to predict period changes in variable stars using noisy and sparse time-series data, while inferring underlying physics and generalizing predictions about cycle-to-cycle variations. Our focus was on Mira variables, a well-known class of pulsating stars. Preprocessing data from Mira, R Andromedae, U Orionis, and Chi Cygni, obtained from the American Association of Variable Star Observers, we predicted luminosity magnitude uncertainty and classified pulsation states. Employing various classification and regression algorithms, along with feature engineering, we aimed to generalize predictions. We created a generalized data set with collective averaged data points, limiting our analysis to a common time duration. Linear regression models yielded no successful predictions, but decision tree and KNN regressors accurately predicted luminosity magnitude errors, indicative of variation over time. Feature engineering successfully aided regression and classification of pulsating star states. After hyper-parameter tuning using Bayesian neural networks, we achieved a classification accuracy of 0.8 and 0.94 for the KNN classifier, respectively, in classifying pulsation states of Mira variables. The regression model achieved an R2 score of 0.98. Our work provides a foundation for developing tools to analyze various pulsating star variables, including Cepheids, RR Lyrae, and Delta Scuti variables, as well as other astrophysical data. These techniques demonstrate impressive performance with time series data sets.