An Information-Theoretic Metric for Transient Classification and Novelty Detection

TL;DR

This paper introduces an information-theoretic metric for transient classification and novelty detection in astronomical surveys, aiding in optimizing observation strategies and resource allocation.

Contribution

It presents a novel cross-entropy based metric tailored for transient science, enhancing population discrimination and detection pipeline optimization.

Findings

Demonstrated the metric's effectiveness in distinguishing object populations

Discussed applications in observing strategy and follow-up resource allocation

Proposed a new approach for novelty detection in astronomical data

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.