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

TL;DR

This paper evaluates LSTM and Phased LSTM neural networks for classifying astronomical lightcurves, finding that combining both units improves classification accuracy across multiple datasets.

Contribution

It provides a comparative analysis of LSTM and Phased LSTM architectures for lightcurve classification and demonstrates the benefits of combining these units.

Findings

LSTM outperformed Phased LSTM in 6 out of 7 datasets.

Combining LSTM and Phased LSTM improves results in all datasets.

The study covers seven diverse astronomical catalogs.

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.