# A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients

arXiv: https://arxiv.org/abs/2103.12102 · HTML: https://mlsanity.com/paper/2103.12102/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 60 references that are in the map.
- First posted on arXiv 22 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [Multivariate Time-series Anomaly Detection via Graph Attention Network](https://mlsanity.com/paper/2009.02040/) (2020)
- [RAPID: Early Classification of Explosive Transients using Deep Learning](https://mlsanity.com/paper/1904.00014/) (2019)
- [Active Anomaly Detection for time-domain discoveries](https://mlsanity.com/paper/1909.13260/) (2019)
- [Classifying Image Sequences of Astronomical Transients with Deep Neural Networks](https://mlsanity.com/paper/2004.13877/) (2020)

## Built on by (most cited first, 11 of 11)

- [Deep Attention-Based Supernovae Classification of Multi-Band Light-Curves](https://mlsanity.com/paper/2201.08482/) (2022)
- [Large Language Models Achieve Gold Medal Performance at the International Olympiad on Astronomy & Astrophysics (IOAA)](https://mlsanity.com/paper/2510.05016/) (2025)
- [ORACLE: A Real-Time, Hierarchical, Deep-Learning Photometric Classifier for the LSST](https://mlsanity.com/paper/2501.01496/) (2025)
- [Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data](https://mlsanity.com/paper/2112.08415/) (2021)
- [A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to Astronomical Time-Series](https://mlsanity.com/paper/2408.08888/) (2024)
- [In Search of the Unknown Unknowns: A Multi-Metric Distance Ensemble for Out of Distribution Anomaly Detection in Astronomical Surveys](https://mlsanity.com/paper/2510.23702/) (2025)
- [Understanding of the properties of neural network approaches for transient light curve approximations](https://mlsanity.com/paper/2209.07542/) (2022)
- [Applications of AI in Astronomy](https://mlsanity.com/paper/2212.01493/) (2022)
- [Self-Supervised Learning for Modeling Gamma-ray Variability in Blazars](https://mlsanity.com/paper/2302.07700/) (2023)
- [Identifying Light-curve Signals with a Deep Learning Based Object Detection Algorithm. II. A General Light Curve Classification Framework](https://mlsanity.com/paper/2311.08080/) (2023)
- [Simulation-Based Pretraining and Domain Adaptation for Astronomical Time Series with Minimal Labeled Data](https://mlsanity.com/paper/2510.12958/) (2025)

## Abstract (arXiv)

There is a shortage of multi-wavelength and spectroscopic followup capabilities given the number of transient and variable astrophysical events discovered through wide-field, optical surveys such as the upcoming Vera C. Rubin Observatory. From the haystack of potential science targets, astronomers must allocate scarce resources to study a selection of needles in real time. Here we present a variational recurrent autoencoder neural network to encode simulated Rubin Observatory extragalactic transient events using 1% of the PLAsTiCC dataset to train the autoencoder. Our unsupervised method uniquely works with unlabeled, real time, multivariate and aperiodic data. We rank 1,129,184 events based on an anomaly score estimated using an isolation forest. We find that our pipeline successfully ranks rarer classes of transients as more anomalous. Using simple cuts in anomaly score and uncertainty, we identify a pure (~95% pure) sample of rare transients (i.e., transients other than Type Ia, Type II and Type Ibc supernovae) including superluminous and pair-instability supernovae. Finally, our algorithm is able to identify these transients as anomalous well before peak, enabling real-time follow up studies in the era of the Rubin Observatory.