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

arXiv: https://arxiv.org/abs/2106.03736 · HTML: https://mlsanity.com/paper/2106.03736/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 71 references that are in the map.
- First posted on arXiv 7 Jun 2021.

## Builds on (most cited first, 13 of 13)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [Rotation-invariant convolutional neural networks for galaxy morphology prediction](https://mlsanity.com/paper/1503.07077/) (2015)
- [Deep Neural Networks to Enable Real-time Multimessenger Astrophysics](https://mlsanity.com/paper/1701.00008/) (2016)
- [Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection](https://mlsanity.com/paper/1701.00458/) (2017)
- [Scalable End-to-end Recurrent Neural Network for Variable star classification](https://mlsanity.com/paper/2002.00994/) (2020)
- [Deep Recurrent Neural Networks for Supernovae Classification](https://mlsanity.com/paper/1606.07442/) (2016)
- [Supervised detection of anomalous light-curves in massive astronomical catalogs](https://mlsanity.com/paper/1404.4888/) (2014)
- [Clustering Based Feature Learning on Variable Stars](https://mlsanity.com/paper/1602.08977/) (2016)
- [Deep-Learnt Classification of Light Curves](https://mlsanity.com/paper/1709.06257/) (2017)
- [An improved quasar detection method in EROS-2 and MACHO LMC datasets](https://mlsanity.com/paper/1304.0401/) (2013)
- [Streaming Classification of Variable Stars](https://mlsanity.com/paper/1912.02235/) (2019)
- [MANTRA: A Machine Learning reference lightcurve dataset for astronomical transient event recognition](https://mlsanity.com/paper/2006.13163/) (2020)

## Built on by (most cited first, 1 of 1)

- [Deep Attention-Based Supernovae Classification of Multi-Band Light-Curves](https://mlsanity.com/paper/2201.08482/) (2022)

## Abstract (arXiv)

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.