KilonovaSCORER: Prior-predictive Scoring of Kilonovae for Real-time Multimessenger Follow-up
KilonovaSCORER:面向实时多信使后随的千新星先验预测评分
Real-time ranking of optical transient candidates during gravitational-wave and multimessenger follow-up is challenging when only sparse early-time, multi-band photometry is available. We present KilonovaSCORER, an open-source framework for scoring and ranking in this regime. It quantifies the consistency of each candidate with a physically motivated kilonova model grid in absolute magnitude space using two complementary per-observation metrics, P<SUB>tail,KNe</SUB> and P<SUB>near,KNe</SUB>. These are aggregated into a cumulative ranking score via inverse-variance weighting in logit space, naturally accounting for heterogeneous observational uncertainties across bands and epochs. A sequential Approximate Bayesian Computation diagnostic tracks photometric consistency across epochs, penalizing candidates whose temporal evolution is incompatible with kilonova expectations. We validate the framework on AT 2017gfo and SN 2025ulz, and test it against supernova simulations under a realistic Rubin/LSST Target-of-Opportunity strategy. The framework recovers kilonova candidates with high confidence while ruling out supernova contaminants within five days of the gravitational-wave trigger. In our LSST ToO simulations, median cumulative scores for thermonuclear and core-collapse supernova contaminants fall to zero by 3─4 days post-trigger, whereas kilonova medians remain ≳0.4. KilonovaSCORER supports real-time workflows for ToO teams and LSST alert brokers, integrates with follow-up coordination platforms such as the Tool for Rapid Object Vetting and Examination, and is publicly available at https://github.com/phelipedarc/KilonovaSCORER/tree/main.
展开 ▾以绝对星等空间的两个互补概率指标在 logit 空间逆方差加权聚合,并加入序贯 ABC 诊断时变一致性;在 LSST ToO 模拟中超新星污染中位分数 3-4 天归零,千新星保持 ≳0.4,可实时集成至 TROVE 等平台。
过去在引力波事件后的光学暂现源筛选中,常依赖颜色-星等截断或启发式准则,易受稀疏早期多波段数据噪声影响。本研究引入基于物理千新星模型网格的先验预测评分,将每个观测的尾概率与近邻概率在 logit 空间逆方差加权,再用序贯近似贝叶斯计算追踪多历元时变一致性,从而把候选体与千新星预期的符合程度自动量化。该框架已对 AT2017gfo 与 SN2025ulz 验证,并在 Rubin/LSST ToO 模拟中表现出对超新星污染的有效剔除能力。未来这类打分方法可集成到 LSST 预警经纪与 ToO 团队的实时工作流,并随模型网格丰富和多波段观测加密不断优化,提升多信使时代千新星证认效率。
接收 2026-07-21 · 刊出 2026-08-06 · 收录 2026-08-20