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.

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• ASTROMER: A transformer-based embedding for the representation of light curves
• Deep Attention-Based Supernovae Classification of Multi-Band Light-Curves
• ATAT: Astronomical Transformer for time series And Tabular data
• Leveraging pre-trained vision Transformers for multi-band photometric light curve classification
• Uncertainty estimation for time series classification: Exploring predictive uncertainty in transformer-based models for variable stars
• Multivariate time series transformer embeddings for light curves of periodic variable stars