The effect of phased recurrent units in the classification of multiple catalogs of astronomical lightcurves

Abstract

In the new era of very large telescopes, where data is crucial to expand scientific knowledge, we have witnessed many deep learning applications for the automatic classification of lightcurves. Recurrent neural networks (RNNs) are one of the models used for these applications, and the LSTM unit stands out for being an excellent choice for the representation of long time series. In general, RNNs assume observations at discrete times, which may not suit the irregular sampling of lightcurves. A traditional technique to address irregular sequences consists of adding the sampling time to the network's input, but this is not guaranteed to capture sampling irregularities during training. Alternatively, the Phased LSTM unit has been created to address this problem by updating its state using the sampling times explicitly. In this work, we study the effectiveness of the LSTM and Phased LSTM based architectures for the classification of astronomical lightcurves. We use seven catalogs containing periodic and nonperiodic astronomical objects. Our findings show that LSTM outperformed PLSTM on 6/7 datasets. However, the combination of both units enhances the results in all datasets.

References in corpus (21)

• The Gaia mission
• Multi-messenger Observations of a Binary Neutron Star Merger
• Gravitational Waves and Gamma-rays from a Binary Neutron Star Merger: GW170817 and GRB 170817A
• The Zwicky Transient Facility: System Overview, Performance, and First Results
• Rotation-invariant convolutional neural networks for galaxy morphology prediction
• Machine Learning for the Zwicky Transient Facility
• A recurrent neural network for classification of unevenly sampled variable stars
• Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection
• The EPOCH Project: I. Periodic variable stars in the EROS-2 LMC database
• The meaning of WISE colours - I. The Galaxy and its satellites
• The expansion field: The value of H_0
• Scalable End-to-end Recurrent Neural Network for Variable star classification
• Supervised detection of anomalous light-curves in massive astronomical catalogs
• De-Trending Time Series for Astronomical Variability Surveys
• An improved quasar detection method in EROS-2 and MACHO LMC datasets
• Unsupervised Classification of Variable Stars
• Uncertain classification of Variable Stars: handling observational GAPS and noise
• ANTARES: A Prototype Transient Broker System
• MANTRA: A Machine Learning reference lightcurve dataset for astronomical transient event recognition
• Streaming Classification of Variable Stars
• Eclipsing Binaries: Tools for Calibrating the Extragalactic Distance Scale