发表机构
Utrecht University; Nikhef; University of Potsdam（乌得勒支大学; 尼赫夫国家粒子物理研究所; 波茨坦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对未来引力波探测器海量双中子星并合事件，提出混合序贯蒙特卡洛算法，实现状态方程后验的批量自适应更新，在单GPU上数小时处理约1500次事件，为实时分层推断提供可行方案。
AI 中文摘要
未来的引力波天文台，如爱因斯坦望远镜，每年将探测到数万次双中子星并合事件，使引力波在未来几十年成为探测中子星状态方程的主要手段。然而，提取这一信息需要对快速增长的事件目录进行分层推断，而现有方法在新事件到达时必须从头开始重新计算，使其在此规模下变得不切实际。在本快报中，我们提出了一种混合序贯蒙特卡洛算法，该算法结合数据和似然退火，以批次方式自适应更新状态方程后验，复用之前的推断结果而非从宽先验重新开始。借助GPU硬件加速，我们的方法在单个GPU上仅需数小时即可从模拟的一个月约1500次双中子星并合事件中推断出状态方程，并预计在当前硬件上可在数天内处理一整年的探测数据。这确立了序贯蒙特卡洛作为未来引力波探测器实时分层推断的实用途径。
英文摘要
Future gravitational-wave observatories such as the Einstein Telescope will detect tens of thousands of binary neutron star mergers per year, making gravitational waves a dominant probe of the neutron-star equation of state in the coming decades. However, extracting this information requires hierarchical inference across a rapidly growing catalog of events, and existing methods must restart from scratch whenever a new event arrives, making them impractical at this scale. In this Letter, we introduce a hybrid sequential Monte Carlo algorithm that combines data and likelihood tempering to adaptively update the equation-of-state posterior in batches, reusing previous inference results instead of restarting from a wide prior. Accelerated by GPU hardware, our method infers the equation of state from a simulated month of around 1500 binary neutron star mergers in a few hours on a single GPU, and we project that a full year of detections could be processed in a few days on current hardware. This establishes sequential Monte Carlo as a practical route for real-time hierarchical inference with future gravitational-wave detectors.
Comments16 pages, 6 figures, comments and feedback welcome
