A detection metric designed for O’Connell effect eclipsing binaries

Abstract

We present the construction of a novel time-domain signature extraction methodology and the development of a supporting supervised pattern detection algorithm. We focus on the targeted identification of eclipsing binaries that demonstrate a feature known as the O'Connell effect. Our proposed methodology maps stellar variable observations to a new representation known as distribution fields (DFs). Given this novel representation, we develop a metric learning technique directly on the DF space that is capable of specif-1 arXiv:1911.03543v1 [astro-ph.IM] 8 Nov 2019 ically identifying our stars of interest. The metric is tuned on a set of labeled eclipsing binary data from the Kepler survey, targeting particular systems exhibiting the O'Connell effect. The result is a conservative selection of 124 potential targets of interest out of the Villanova Eclipsing Binary Catalog. Our framework demonstrates favorable performance on Kepler eclipsing binary data, taking a crucial step in preparing the way for large-scale data volumes from next-generation telescopes such as LSST and SKA.

Variable star classification using multiview metric learning

“…SSMM itself is an effective feature for discriminating variable star types as shown by Johnston and Peter (2017). Similarly, DF has been shown to be a valuable feature for discriminating time domain signatures, see Helfer et al (2015) and Johnston et al (2019).…”

“…Although our initial goal is variable star identification, given a separate set of features this method could be applied to other astroinformatics problems (i.e., image classification for galaxies, spectral identification for stars or comets, etc.). While we demonstrate the classifier has a multi-class classification design, which is common in the astroinformatics references we have provided, the design here can easily be transformed into a one-vs-all design (Johnston and Oluseyi 2017) for the purposes of generating a detector or classifier designed specifically to a user's needs (Johnston et al 2019).…”

“…Prior studies have initially addressed the potential of using metric learning as a means for classification of variable stars (Johnston et al 2019). Metric learning has a number of benefits that are advantageous to the astronomer:…”

“…Presented here is a methodology that addresses these issues using a combination of new features and advanced classifiers designs. Two novel transforms, Slotted Symbolic Markov Model (SSMM, Johnston and Peter 2017) and Distribution Fields (DF, Johnston et al 2019), are used to generate viable feature spaces for the classification of variable stars. SSMM requires no phasing of the time domain data but still provides a feature that is shape based, DF allows for the consideration of the whole phased waveform without additional picking and choosing of metrics from the waveform (i.e., see Helfer et al 2015).…”

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).…”