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arXiv · 2604.13207

An Information-Theoretic Metric for Transient Classification and Novelty Detection

Yu-Qian·Ouyang·Alex I. Malz·Ming Lian·Shar Daniels·Federica Bianco·Mathilda Nilsson
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Abstract

The development of the observing strategy for the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) requires a broad optimization across science cases inside and outside of time-domain astronomy. We introduce a novel metric for transient science with LSST based on information-theoretic cross-entropy. We demonstrate its utility for distinguishing populations of objects and discuss applications for observing strategy / detection pipeline optimization as well as novelty detection and follow-up resource allocation.

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Yu-Qian, Ouyang, Alex I. Malz, Ming Lian, Shar Daniels, Federica Bianco, Mathilda Nilsson. 2026-04-14. An Information-Theoretic Metric for Transient Classification and Novelty Detection. https://arxiv.org/abs/2604.13207

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