# A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to Astronomical Time-Series

arXiv: https://arxiv.org/abs/2408.08888 · HTML: https://mlsanity.com/paper/2408.08888/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 5 Aug 2024.

## Builds on (most cited first, 8 of 8)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [RAPID: Early Classification of Explosive Transients using Deep Learning](https://mlsanity.com/paper/1904.00014/) (2019)
- [Practical Galaxy Morphology Tools from Deep Supervised Representation Learning](https://mlsanity.com/paper/2110.12735/) (2021)
- [A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients](https://mlsanity.com/paper/2103.12102/) (2021)
- [Active Anomaly Detection for time-domain discoveries](https://mlsanity.com/paper/1909.13260/) (2019)
- [Real-Time Detection of Anomalies in Large-Scale Transient Surveys](https://mlsanity.com/paper/2111.00036/) (2021)

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

- [A Multi-directional Meta-Learning Framework for Class-Generalizable Anomaly Detection](https://mlsanity.com/paper/2601.19833/) (2026)

## Abstract (arXiv)

Automating anomaly detection is an open problem in many scientific fields, particularly in time-domain astronomy, where modern telescopes generate millions of alerts per night. Currently, most anomaly detection algorithms for astronomical time-series rely either on hand-crafted features or on features generated through unsupervised representation learning, coupled with standard anomaly detection algorithms. In this work, we introduce a novel approach that leverages the latent space of a neural network classifier for anomaly detection. We then propose a new method called Multi-Class Isolation Forests (MCIF), which trains separate isolation forests for each class to derive an anomaly score for an object based on its latent space representation. This approach significantly outperforms a standard isolation forest when distinct clusters exist in the latent space. Using a simulated dataset emulating the Zwicky Transient Facility (54 anomalies and 12,040 common), our anomaly detection pipeline discovered 46±3 anomalies (~ 85% recall) after following up the top 2,000 (~ 15%) ranked objects. Furthermore, our classifier-based approach outperforms or approaches the performance of other state-of-the-art anomaly detection pipelines. Our novel method demonstrates that existing and new classifiers can be effectively repurposed for real-time anomaly detection. The code used in this work, including a Python package, is publicly available, https://github.com/Rithwik-G/AstroMCAD.