SkyMapper Optical Follow-up of Gravitational Wave Triggers: Alert Science Data Pipeline and LIGO/Virgo O3 Run

TL;DR

This paper describes the SkyMapper follow-up system for gravitational wave events, including a new software pipeline and machine learning classifier, successfully tested on GW190425, aiming to detect kilonovae up to 200 Mpc.

Contribution

The paper introduces a real-time data pipeline and machine learning classifier for optical follow-up of gravitational wave events, enhancing detection efficiency and candidate filtering.

Findings

High completeness (~98%) and purity (~91%) of transient candidate classification.

Reduction of candidates by over 10 times through filtering.

Successful demonstration with GW190425 data.

Abstract

We present an overview of the SkyMapper optical follow-up program for gravitational-wave event triggers from the LIGO/Virgo observatories, which aims at identifying early GW170817-like kilonovae out to $\sim 200$ Mpc distance. We describe our robotic facility for rapid transient follow-up, which can target most of the sky at $\delta<+10\deg $ to a depth of $i_\mathrm{AB}\approx 20$ mag. We have implemented a new software pipeline to receive LIGO/Virgo alerts, schedule observations and examine the incoming real-time data stream for transient candidates. We adopt a real-bogus classifier using ensemble-based machine learning techniques, attaining high completeness ($\sim$98%) and purity ($\sim$91%) over our whole magnitude range. Applying further filtering to remove common image artefacts and known sources of transients, such as asteroids and variable stars, reduces the number of candidates by a factor of more than 10. We demonstrate the system performance with data obtained for GW190425, a binary neutron star merger detected during the LIGO/Virgo O3 observing campaign. In time for the LIGO/Virgo O4 run, we will have deeper reference images allowing transient detection to $i_\mathrm{AB}\approx $21 mag.