Scalable end-to-end recurrent neural network for variable star classification

Abstract

During the last decade, considerable effort has been made to perform automatic classification of variable stars using machine learning techniques. Traditionally, light curves are represented as a vector of descriptors or features used as input for many algorithms. Some features are computationally expensive, cannot be updated quickly and hence for large datasets such as the LSST cannot be applied. Previous work has been done to develop alternative unsupervised feature extraction algorithms for light curves, but the cost of doing so still remains high. In this work, we propose an end-to-end algorithm that automatically learns the representation of light curves that allows an accurate automatic classification. We study a series of deep learning architectures based on Recurrent Neural Networks and test them in automated classification scenarios. Our method uses minimal data preprocessing, can be updated with a low computational cost for new observations and light curves, and can scale up to massive datasets. We transform each light curve into an input matrix representation whose elements are the differences in time and magnitude, and the outputs are classification probabilities. We test our method in three surveys: OGLE-III, Gaia and WISE. We obtain accuracies of about 95% in the main classes and 75% in the majority of subclasses. We compare our results with the Random Forest classifier and obtain competitive accuracies while being faster and scalable. The analysis shows that the computational complexity of our approach grows up linearly with the light curve size, while the traditional approach cost grows as N log (N).

Deep Modeling of Quasar Variability

“…This means that information is retained about what has been calculated so far and this can affect the current calculation and prediction. RNNs have been used in astronomy for time series classification (Charnock & Moss 2017;Naul et al 2018;Becker et al 2020). The RNN AE network is trained with time series as input to reproduce the same time series as the output.…”

Light-curve classification with recurrent neural networks for GOTO: dealing with imbalanced data

“…In contrast to feature-based ML models, DL models are able to learn salient features from the data, and do not require a feature extraction step prior to training. Deep neural network and recurrent neural network (RNN) architectures have been used to classify simulated light curves (Möller & de Boissière 2020;Pasquet et al 2019;Charnock & Moss 2017) and real light curves (Takahashi et al 2020) for supernova classification, general transient classification (Muthukrishna et al 2019), and variable star classification with real light curves (Becker et al 2020;Tsang & Schultz 2019;Mahabal et al 2017). Work has also been done on classifying objects using image stamps as input to convolutional neural networks (Wardęga et al 2020;Gómez et al 2020;Carrasco-Davis et al 2019).…”

Development of accurate classification of heavenly bodies using novel machine learning techniques

“…Many researchers focused on star-quasar (Zhang et al 2011;Jin et al 2019;Zhang et al 2009Zhang et al , 2013Viquar et al 2018), galaxy-quasar (Bailer-Jones et al 2019 or star-galaxy Philip et al (2002) binary classification. Others (López et al 2010, Becker et al 2020) focused on multi-class classification of stars, galaxies and quasars Cabanac et al (2002); Acharya et al (2018). In these works, various methods have been applied to automatically classify the heavenly bodies accurately.…”

“…Many databases related to sky survey data are freely available. Among them, most popular databases are SDSS (Zhang et al 2011(Zhang et al , 2013(Zhang et al , 2009Viquar et al 2018, Acharya et al 2018), Gaia (Bailer-Jones et al 2019Becker et al 2020), WISE (Jin et al 2019;Becker et al 2020) and UKIDSS (Zhang et al 2011(Zhang et al , 2013. The summary of related works conducted using these databases is shown in Table 1 Zhang et al ( 2011) operated on the data from UKIDSS database.…”

“…They focused on multi-class classification of different categories of stars. Becker et al (2020) worked on similar problem. They proposed an end-to-end solution for automated source classification based on RNNs.…”