Using Machine Learning for Discovery in Synoptic Survey Imaging

TL;DR

This paper presents a machine learning framework for real-time identification of astrophysical variability in large-scale sky surveys, improving detection accuracy amidst noise and artefacts.

Contribution

The work introduces a probabilistic ML approach with optimized features that is robust to training data contamination, enhancing discovery efficiency in time-domain astronomy.

Findings

Achieved at most 7.7% missed detection rate at 1% false-positive rate.

Developed a feature set of 23 attributes from an initial 42, avoiding over-fitting.

Framework is tolerant to up to 10% label contamination in training data.

Abstract

Modern time-domain surveys continuously monitor large swaths of the sky to look for astronomical variability. Astrophysical discovery in such data sets is complicated by the fact that detections of real transient and variable sources are highly outnumbered by bogus detections caused by imperfect subtractions, atmospheric effects and detector artefacts. In this work we present a machine learning (ML) framework for discovery of variability in time-domain imaging surveys. Our ML methods provide probabilistic statements, in near real time, about the degree to which each newly observed source is astrophysically relevant source of variable brightness. We provide details about each of the analysis steps involved, including compilation of the training and testing sets, construction of descriptive image-based and contextual features, and optimization of the feature subset and model tuning parameters. Using a validation set of nearly 30,000 objects from the Palomar Transient Factory, we demonstrate a missed detection rate of at most 7.7% at our chosen false-positive rate of 1% for an optimized ML classifier of 23 features, selected to avoid feature correlation and over-fitting from an initial library of 42 attributes. Importantly, we show that our classification methodology is insensitive to mis-labelled training data up to a contamination of nearly 10%, making it easier to compile sufficient training sets for accurate performance in future surveys. This ML framework, if so adopted, should enable the maximization of scientific gain from future synoptic survey and enable fast follow-up decisions on the vast amounts of streaming data produced by such experiments.