We present an overview of the SkyMapper optical follow-up programme for gravitational-wave event triggers from the LIGO/Virgo observatories, which aims at identifying early GW170817-like kilonovae out to $\sim200\,\mathrm{Mpc}$ 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 $i_\mathrm{AB}\approx 20\,\mathrm{mag}$ . 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 ( $\sim98\%$ ) and purity ( $\sim91\%$ ) 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 $i_\mathrm{AB}\approx 21\,\mathrm{mag}$ .

中文翻译：

---

SkyMapper 对引力波触发器的光学跟踪：警报科学数据管道和 LIGO/Virgo O3 运行

我们概述了来自 LIGO/Virgo 天文台的引力波事件触发的 SkyMapper 光学后续计划，该计划旨在识别早期类似 GW170817 的千新星 $\sim200\,\mathrm{Mpc}$ 距离。我们描述了我们用于快速瞬态跟踪的机器人设施，它可以瞄准大部分天空 $\delta<+10\度 $ 到深度 $i_\mathrm{AB}\大约 20\,\mathrm{mag}$ . 我们已经实施了一个新的软件管道来接收 LIGO/Virgo 警报、安排观察并检查传入的实时数据流以查找临时候选人。我们采用基于集成的机器学习技术的真伪分类器，获得了很高的完整性（ $\sim98\%$ ) 和纯度 ( $\sim91\%$ ) 在我们的整个幅度范围内。应用进一步的过滤来去除常见的图像伪影和已知的瞬变源，例如小行星和变星，将候选者的数量减少了 10 倍以上。我们用 GW190425（双中子星合并）获得的数据展示了系统性能在 LIGO/Virgo O3 观测活动期间检测到。在 LIGO/Virgo O4 运行期间，我们将获得更深的参考图像，从而允许瞬态检测 $i_\mathrm{AB}\大约 21\,\mathrm{mag}$ .