A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to Astronomical Time-Series
<|reference_start|>A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to Astronomical Time-Series: 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 $46\pm3$ anomalies ($\sim 85\%$ recall) after following up the top 2,000 ($\sim 15\%$) 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.<|reference_end|>
@article{gupta2024a,
    title={A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to
  Astronomical Time-Series},
    author={Rithwik Gupta, Daniel Muthukrishna and Michelle Lochner},
    journal={arXiv preprint arXiv:2408.08888},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08888},
    primaryClass={cs.LG astro-ph.HE astro-ph.IM}
}