An Information-Theoretic Metric for Transient Classification and Novelty Detection

Fuente: arXiv
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Main Authors: Yu-Qian, Ouyang, Malz, Alex I., Lian, Ming, Daniels, Shar, Bianco, Federica, Nilsson, Mathilda
Format: Preprint
Published: 2026
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author Yu-Qian
Ouyang
Malz, Alex I.
Lian, Ming
Daniels, Shar
Bianco, Federica
Nilsson, Mathilda
author_facet Yu-Qian
Ouyang
Malz, Alex I.
Lian, Ming
Daniels, Shar
Bianco, Federica
Nilsson, Mathilda
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2604_13207
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle An Information-Theoretic Metric for Transient Classification and Novelty Detection
Yu-Qian
Ouyang
Malz, Alex I.
Lian, Ming
Daniels, Shar
Bianco, Federica
Nilsson, Mathilda
Instrumentation and Methods for Astrophysics
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.
title An Information-Theoretic Metric for Transient Classification and Novelty Detection
topic Instrumentation and Methods for Astrophysics
url https://arxiv.org/abs/2604.13207