Identification of anomalous light curves within time-domain surveys is often challenging. In addition, with the growing number of wide-field surveys and the volume of data produced exceeding astronomers ability for manual evaluation, outlier and anomaly detection is becoming vital for transient science. We present an unsupervised method for transient discovery using a clustering technique and the Astronomaly package. As proof of concept, we evaluate 85553 minute-cadenced light curves collected over two 1.5 hour periods as part of the Deeper, Wider, Faster program, using two different telescope dithering strategies. By combining the clustering technique HDBSCAN with the isolation forest anomaly detection algorithm via the visual interface of Astronomaly, we are able to rapidly isolate anomalous sources for further analysis. We successfully recover the known variable sources, across a range of catalogues from within the fields, and find a further 7 uncatalogued variables and two stellar flare events, including a rarely observed ultra fast flare (5 minute) from a likely M-dwarf.
@article{arxiv.2008.04666,
title = {Unsupervised machine learning for transient discovery in Deeper, Wider, Faster light curves},
author = {Sara Webb and Michelle Lochner and Daniel Muthukrishna and Jeff Cooke and Chris Flynn and Ashish Mahabal and Simon Goode and Igor Andreoni and Tyler Pritchard and Timothy M. C. Abbott},
journal= {arXiv preprint arXiv:2008.04666},
year = {2020}
}