Automated Transient Identification in the Dark Energy Survey

TL;DR

This paper presents a machine learning algorithm using Random Forests to improve transient detection in optical images, significantly reducing false candidates while maintaining high detection efficiency in the Dark Energy Survey.

Contribution

The paper introduces a novel supervised machine learning approach for transient identification that enhances efficiency and reduces false positives in large-scale sky surveys.

Findings

Transient candidate reduction by a factor of 13.4

Loss of only 1% of artificial supernovae, mostly very faint

Algorithm performance characterized in detail

Abstract

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. 2013 through Feb. 2014) using the algorithm, the number of transient candidates eligible for human scanning decreased by a factor of 13.4, while only 1 percent of the artificial Type Ia supernovae (SNe) injected into search images to monitor survey efficiency were lost, most of which were very faint events. Here we characterize the algorithm's performance in detail, and we discuss how it can inform pipeline design decisions for future time-domain imaging surveys, such as the Large Synoptic Survey Telescope and the Zwicky Transient Facility. An implementation of the algorithm and the training data used in this paper are available at http://portal.nersc.gov/project/dessn/autoscan.