Unsupervised machine learning for transient discovery in deeper, wider, faster light curves

View/ Open
Date
2020Author
Lochner, Michelle
Webb, Sara
Muthukrishna, Daniel
Metadata
Show full item recordAbstract
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 85 553 min-cadenced light curves collected over two ∼1.5 h periods as part of the Deeper, Wider, Faster program, using two different telescope dithering strategies.