面向多信使瞬变源的RADAR自主射电后随观测：从警报解析到推断与观测调度
Toward Autonomous Radio Follow-up of Multi-messenger Transients with RADAR: From Alert Parsing to Inference and Observation Scheduling
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中文总结 AI 辅助
本文扩展了RADAR框架，通过基准测试三种大语言模型、引入40倍加速的并发似然评估，并实现LLM驱动的观测调度，推动引力波射电后随观测走向自主化。
中文摘要 AI 辅助
多信使天文学（MMA）通过引力波（GW）、电磁（EM）辐射、中微子和宇宙射线对宇宙源进行联合研究，正在迅速重塑时域天体物理学。实现MMA的愿景需要协调异构观测资源，并自动化从警报到分析再到后随观测的链条。RADAR（射电余辉探测与AI驱动响应）是一个用于引力波事件射电后随观测的联邦式、隐私增强框架，此前已在GW170817上得到验证。在此，我们沿三个方向对其进行扩展。首先，我们针对GW170817射电光变曲线数据集，对三个大语言模型（LLM；GPT-5.5、Claude-Opus-4.7和Gemini-3.5-Flash）进行了基准测试。GPT-5.5在事件级别取得了最高的$F_1$分数（精确率与召回率的调和平均值），为$0.893 \pm 0.010$，并取得了最高的GCN级别召回率$0.794 \pm 0.013$，分别比之前的GPT-4.1结果提高了16%和10%，而Claude-Opus-4.7取得了最高的精确率$0.978 \pm 0.014$。其次，我们引入了并发似然评估，将MCMC计算速度相比我们之前的结果提升了$40\times$。第三，我们提出了一种由LLM驱动的方案，可将自然语言观测请求转换为可直接提交给Karl G. Jansky甚大阵列的调度块。这些进展共同推动RADAR朝着用于引力波射电后随观测的可扩展、高度自主的系统迈进。
英文摘要
Multi-messenger astronomy (MMA), the joint study of cosmic sources through gravitational waves (GWs), electromagnetic (EM) radiation, neutrinos, and cosmic rays, is rapidly reshaping time-domain astrophysics. Realizing the promise of MMA will require coordinating heterogeneous observing resources and automating the chain from alert to analysis to follow-up. RADAR (Radio Afterglow Detection and AI-driven Response) is a federated, privacy-enhancing framework for the radio follow-up of GW events, previously validated on GW170817. Here, we extend it along three axes. First, we benchmark three large language models (LLMs; GPT-5.5, Claude-Opus-4.7, and Gemini-3.5-Flash) against the GW170817 radio light curve dataset. GPT-5.5 attains the highest event-level $F_1$ score, the harmonic mean of precision and recall, at $0.893 \pm 0.010$, and the highest GCN-level recall, $0.794 \pm 0.013$, improving on previous GPT-4.1 results by 16\% and 10\%, respectively, while Claude-Opus-4.7 achieves the highest precision, $0.978 \pm 0.014$. Second, we introduce concurrent likelihood evaluation, which speeds up the MCMC computation by a factor of $40\times$ over our previous results. Third, we present an LLM-driven scheme that converts natural-language observing requests into submission-ready scheduling blocks for the Karl G. Jansky Very Large Array. Together, these developments advance RADAR toward a scalable, largely autonomous system for GW radio follow up.
发表机构
- The University of Chicago（芝加哥大学）
- Argonne National Laboratory（阿贡国家实验室）
- Johns Hopkins University（约翰斯·霍普金斯大学）
- University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。
