The effect of phased recurrent units in the classification of multiple catalogs of astronomical lightcurves
This work addresses the classification of irregularly sampled astronomical data for astronomers, but it is incremental as it builds on existing methods without introducing a new paradigm.
The study tackled the problem of classifying astronomical lightcurves using recurrent neural networks, specifically comparing LSTM and Phased LSTM units, and found that while LSTM outperformed Phased LSTM on 6 out of 7 datasets, combining both units improved results across all datasets.
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.