Anomaly Detection to identify Transients in LSST Time Series Data
arXiv:2503.09699 · doi:10.1093/mnras/staf1477
Abstract
We introduce a novel approach to detecting microlensing events and other transients in light curves, utilising the isolation forest (iForest) algorithm for anomaly detection. Focusing on the Legacy Survey of Space and Time by the Vera C. Rubin Observatory, we show that an iForest trained on signal-less light curves can efficiently identify microlensing events by different types of dark objects and binaries, as well as variable stars. We further show that the iForest has real-time applicability through a drip-feed analysis, demonstrating its potential as a valuable tool for LSST alert brokers to efficiently prioritise and classify transient candidates for follow-up observations.
8 pages, 7 figures. The dataset and the artefacts for the trained models are available at https://zenodo.org/records/15005108
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