Machine Learning-based Brokers for Real-time Classification of the LSST Alert Stream

TL;DR

This paper presents a machine learning pipeline for real-time classification of astronomical transient and variable sources using LSST alert streams, validated on real observational data to improve follow-up prioritization.

Contribution

It introduces a novel ML pipeline tailored for early, intermediate, and retrospective classification of alerts, validated on real data for the first time.

Findings

High classification accuracy on real, sparse data

Effective early and retrospective alert categorization

Progress towards real-time implementation for LSST

Abstract

The unprecedented volume and rate of transient events that will be discovered by the Large Synoptic Survey Telescope (LSST) demands that the astronomical community update its followup paradigm. Alert-brokers -- automated software system to sift through, characterize, annotate and prioritize events for followup -- will be critical tools for managing alert streams in the LSST era. The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is one such broker. In this work, we develop a machine learning pipeline to characterize and classify variable and transient sources only using the available multiband optical photometry. We describe three illustrative stages of the pipeline, serving the three goals of early, intermediate and retrospective classification of alerts. The first takes the form of variable vs transient categorization, the second, a multi-class typing of the combined variable and transient dataset, and the third, a purity-driven subtyping of a transient class. While several similar algorithms have proven themselves in simulations, we validate their performance on real observations for the first time. We quantitatively evaluate our pipeline on sparse, unevenly sampled, heteroskedastic data from various existing observational campaigns, and demonstrate very competitive classification performance. We describe our progress towards adapting the pipeline developed in this work into a real-time broker working on live alert streams from time-domain surveys.