Summary of A Classifier-based Approach to Multi-class Anomaly Detection Applied to Astronomical Time-series, by Rithwik Gupta et al.
A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to Astronomical Time-Series
by Rithwik Gupta, Daniel Muthukrishna, Michelle Lochner
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: High Energy Astrophysical Phenomena (astro-ph.HE); Instrumentation and Methods for Astrophysics (astro-ph.IM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to automating anomaly detection in time-domain astronomy combines the latent space of a neural network classifier with isolation forests for efficient and accurate identification of anomalies. The Multi-Class Isolation Forests (MCIF) method trains separate isolation forests for each class, leveraging the latent space representation to derive an anomaly score. This technique outperforms standard isolation forests when distinct clusters exist in the latent space. A simulated dataset emulating the Zwicky Transient Facility was used to evaluate the pipeline’s performance, achieving a recall of 85% by following up the top-ranked objects. The approach demonstrates that existing and new classifiers can be repurposed for real-time anomaly detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Astronomers use special computers to look at stars and other celestial bodies. They often find things that are strange or unusual, like a star that is acting differently than usual. Right now, humans have to look at the data and decide what’s normal and what’s not. A team of researchers has come up with a new way to do this using special computers and math. Their method looks at how different stars are from each other and uses that information to find the unusual ones. They tested their method on some pretend data and found 85% of the unusual things. This is important because it could help us learn more about the universe and maybe even discover new planets or stars. |
Keywords
» Artificial intelligence » Anomaly detection » Latent space » Neural network » Recall
