Real-Time Active Learning for optimised spectroscopic follow-up: Enhancing early SN Ia classification with the Fink broker

TL;DR

This paper demonstrates a real-time active learning approach to optimize spectroscopic follow-up for early SN Ia classification, improving training sets and classification accuracy with fewer spectra in the context of large astronomical surveys.

Contribution

It introduces the first real-time active learning application for spectroscopic follow-up, enhancing early SN Ia classification and training set quality using the FINK broker and ZTF data.

Findings

Active learning improves classification metrics with 25% fewer spectra.

Follow-up strategy selects fainter and diverse transient events.

Method is effective for constructing optimal training samples in astronomy.

Abstract

Current and future surveys rely on machine learning classification to obtain large and complete samples of transients. Many of these algorithms are restricted by training samples that contain a limited number of spectroscopically confirmed events. Here, we present the first real-time application of Active Learning to optimise spectroscopic follow-up with the goal of improving training sets of early type Ia supernovae (SNe Ia) classifiers. Using a photometric classifier for early SN Ia, we apply an Active Learning strategy for follow-up optimisation using the real-time FINK broker processing of the ZTF public stream. We perform follow-up observations at the ANU 2.3m telescope in Australia and obtain 92 spectroscopic classified events that are incorporated in our training set. We show that our follow-up strategy yields a training set that, with 25% less spectra, improves classification metrics when compared to publicly reported spectra. Our strategy selects in average fainter events and, not only supernovae types, but also microlensing events and flaring stars which are usually not incorporated on training sets. Our results confirm the effectiveness of active learning strategies to construct optimal training samples for astronomical classifiers. With the Rubin Observatory LSST soon online, we propose improvements to obtain earlier candidates and optimise follow-up. This work paves the way to the deployment of real-time AL follow-up strategies in the era of large surveys.