Unsupervised machine learning for transient discovery in Deeper, Wider, Faster light curves

TL;DR

This paper introduces an unsupervised clustering and anomaly detection method using Astronomaly to identify transient and variable sources in large astronomical light curve datasets, successfully recovering known variables and discovering new phenomena.

Contribution

The paper presents a novel unsupervised approach combining HDBSCAN and isolation forest within Astronomaly for efficient transient discovery in large-scale light curve data.

Findings

Successfully recovered known variable sources

Discovered 7 uncatalogued variables and 2 stellar flares

Detected a rare 5-minute ultra-fast flare from an M-dwarf

Abstract

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.