# Alert Classification for the ALeRCE Broker System: The Real-time Stamp Classifier

arXiv: https://arxiv.org/abs/2008.03309 · HTML: https://mlsanity.com/paper/2008.03309/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2020.

## Built on by (most cited first, 10 of 10)

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- [Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks](https://mlsanity.com/paper/2205.06758/) (2022)
- [Deep Attention-Based Supernovae Classification of Multi-Band Light-Curves](https://mlsanity.com/paper/2201.08482/) (2022)
- [What's the Difference? The potential for Convolutional Neural Networks for transient detection without template subtraction](https://mlsanity.com/paper/2203.07390/) (2022)
- [Domain Adaptation via Minimax Entropy for Real/Bogus Classification of Astronomical Alerts](https://mlsanity.com/paper/2308.07538/) (2023)
- [Textual interpretation of transient image classifications from large language models](https://mlsanity.com/paper/2510.06931/) (2025)
- [ORACLE: A Real-Time, Hierarchical, Deep-Learning Photometric Classifier for the LSST](https://mlsanity.com/paper/2501.01496/) (2025)
- [StarEmbed: Benchmarking Time Series Foundation Models on Astronomical Observations of Variable Stars](https://mlsanity.com/paper/2510.06200/) (2025)
- [Temporal Stamp Classifier: Classifying Short Sequences of Astronomical Alerts](https://mlsanity.com/paper/2405.15073/) (2024)
- [Pre-training vision models for the classification of alerts from wide-field time-domain surveys](https://mlsanity.com/paper/2512.11957/) (2025)

## Abstract (arXiv)

We present a real-time stamp classifier of astronomical events for the ALeRCE (Automatic Learning for the Rapid Classification of Events) broker. The classifier is based on a convolutional neural network, trained on alerts ingested from the Zwicky Transient Facility (ZTF). Using only the textitscience, reference and textitdifference images of the first detection as inputs, along with the metadata of the alert as features, the classifier is able to correctly classify alerts from active galactic nuclei, supernovae (SNe), variable stars, asteroids and bogus classes, with high accuracy (~94%) in a balanced test set. In order to find and analyze SN candidates selected by our classifier from the ZTF alert stream, we designed and deployed a visualization tool called SN Hunter, where relevant information about each possible SN is displayed for the experts to choose among candidates to report to the Transient Name Server database. From June 26th 2019 to February 28th 2021, we have reported 6846 SN candidates to date (11.8 candidates per day on average), of which 971 have been confirmed spectroscopically. Our ability to report objects using only a single detection means that 70% of the reported SNe occurred within one day after the first detection. ALeRCE has only reported candidates not otherwise detected or selected by other groups, therefore adding new early transients to the bulk of objects available for early follow-up. Our work represents an important milestone toward rapid alert classifications with the next generation of large etendue telescopes, such as the Vera C. Rubin Observatory.