# Temporal Stamp Classifier: Classifying Short Sequences of Astronomical Alerts

arXiv: https://arxiv.org/abs/2405.15073 · HTML: https://mlsanity.com/paper/2405.15073/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 23 May 2024.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Alert Classification for the ALeRCE Broker System: The Real-time Stamp Classifier](https://mlsanity.com/paper/2008.03309/) (2020)
- [Deep Attention-Based Supernovae Classification of Multi-Band Light-Curves](https://mlsanity.com/paper/2201.08482/) (2022)
- [A Physics inspired Functional Operator for Model Uncertainty Quantification in the RKHS](https://mlsanity.com/paper/2109.10888/) (2021)

## Abstract (arXiv)

In this work, we propose a deep learning-based classification model of astronomical objects using alerts reported by the Zwicky Transient Facility (ZTF) survey. The model takes as inputs sequences of stamp images and metadata contained in each alert, as well as features from the All-WISE catalog. The proposed model, called temporal stamp classifier, is able to discriminate between three classes of astronomical objects: Active Galactic Nuclei (AGN), Super-Novae (SNe) and Variable Stars (VS), with an accuracy of approximately 98% in the test set, when using 2 to 5 detections. The results show that the model performance improves with the addition of more detections. Simple recurrence models obtain competitive results with those of more complex models such as LSTM.We also propose changes to the original stamp classifier model, which only uses the first detection. The performance of the latter model improves with changes in the architecture and the addition of random rotations, achieving a 1.46% increase in test accuracy.