Structural properties and classification of variable stars: A study through unsupervised machine learning techniques

Abstract

The advancement in the field of data science especially in machine learning along with vast databases of variable star projects like the Optical Gravitational Lensing Experiment (OGLE) encourages researchers to analyse as well as classify light curves of different variable stars automatically with efficiency. In the present work, we have demonstrated the relative performances of principal component analysis (PCA) and independent component analysis (ICA) applying to huge databases of OGLE variable star light curves after obtaining 1000 magnitudes between phase 0 to 1 with step length 0.001 for each light curves in identifying resonances for fundamental mode (FU) and first overtone (FO) Cepheids and in the classification of variable stars for Large Magellanic Cloud (LMC), Small Magellanic Cloud (SMC) as well as Milky Way (MW). We have seen that the performance of ICA is better for finding resonances for Cepheid variables as well as for accurately classifying large data sets of light curves than PCA. Using K-means clustering algorithm (CA) with respect to independent components (ICs), we have plotted periodluminosity diagrams and colour-magnitude diagrams separately for LMC, SMC and MW and found that ICA along with K-means CA is a very robust tool for classification as well as future prediction on the nature of light curves of variable stars.

Classifying Kepler light curves for 12,000 A and F stars using supervised feature-based machine learning

“…Considerable efforts have gone into using machine learning to classify light curves from large ground-based surveys (e.g., Carrasco-Davis et al 2019;Tsang & Schultz 2019;Johnston et al 2019a;Cabral et al 2020;Hosenie et al 2020;Jamal & Bloom 2020;Szklenár et al 2020;Bassi et al 2021;Zhang & Bloom 2021). Such techniques have also been applied to light curves from NASA's Kepler and K2 missions (e.g., Blomme et al 2010Blomme et al , 2011Debosscher et al 2011;Bass & Borne 2016;Armstrong et al 2016;Hon et al 2017Hon et al , 2018bJohnston et al 2019b;Kgoadi et al 2019;Le Saux et al 2019;Giles & Walkowicz 2020;Kuszlewicz et al 2020;Audenaert et al 2021;Paul & Chattopadhyay 2022).…”