Automated Transient Identification in the Dark Energy Survey

Understand

We describe an algorithm for identifying point-source transients and moving objects on reference-subtracted optical images containing artifacts of processing and instrumentation.

• The algorithm makes use of the supervised machine learning technique known as Random Forest.
• We present results from its use in the Dark Energy Survey Supernova program (DES-SN), where it was trained using a sample of 898,963 signal and background events generated by the transient detection pipeline.
• After reprocessing the data collected during the first DES-SN observing season (Sep.