The classification of periodic light curves from non-survey optimized observational data through automated extraction of phase-based visual features

Abstract

Abstract-We present Random Forest, Support Vector Machine and Feedforward Neural Network models to classify 2519 variable star light curves. These light curves are generated from a reduction of non-survey optimized observational images gathered by wide-field cameras mounted on the Liverpool Telescope. We extract 16 features found to be highly informative in previous studies and achieve an area under the curve of 0.8495 using a feedforward neural network with 50 hidden neurons trained with stratified 10-fold cross-validation with 3 repeats. We propose using an automated visual feature extraction technique by transforming bin-averaged phase-folded light curves into image based representations. This eliminates much of the noise and the missing phase data, due to sampling defects, should have a less destructive effect on these shape features as they still remain at least partially present. There is also no need for feature engineering as the learning algorithms can learn shape features directly from the light curves. We produced a set of scaled images based on a threshold of data points in each pixel. Training on the same feedforward network, we achieve an area under the curve of 0.6348. By introducing the Period and Amplitude as features into this dataset therefore giving meaning to the dimensions of the image we show this improves to 0.7952. Our current models lack translational-invariance and the method may be better suited to specific sub-classification problems common in the variable object hierarchical multi-class problem.

Feature-Based Classification Neural Network for Kepler Light Curves from Quarter 1

“…The higher classification accuracy can be obtained by using features that contain more information about the light curves [16,20]. In addition to the frequency features, the standard deviation of the flux and median of the flux are calculated.…”

“…Therefore, a more favorable classification result is indicated when the curve is closer to the upper-left corner [20]. The area under the ROC curve (AUC) serves as a performance metric for machine learning algorithms [34].…”

Variable star classification using multiview metric learning

“…Likewise, standard classification techniques in the astroinformatics-community span a few areas: (i) the classifier is designed such that the user selects features and the classifier is trained on variables with a known type ("expert selected features, for correlation discovery", Debosscher 2009;Sesar et al 2011;Richards et al 2012;Graham et al 2013a;Armstrong et al 2016;Mahabal et al 2017;Hinners et al 2018), (ii) the classifier is designed such that the computer selects the optimal features and the classifier is trained on variables with a known type (McWhirter et al 2017;Naul et al 2018, "computer selected features, for correlation discovery"), (iii) the classifier (clustering algorithm) is designed such that that user selects features and variables with an unknown type are provided ("expert selected features, for class discovery", Valenzuela and Pichara 2018; Modak et al 2018).…”