  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.
