AI 中文总结
该研究提出GPU原生嵌套采样内核，基于SwiG结构优化随机采样技术，实现了LIGO/Virgo探测器引力波信号的快速参数估计，部分场景达实时水平。
AI 中文摘要
我们提出了一种专门针对引力波推断问题快速参数估计的GPU原生嵌套采样内核。基于用于快速混合的Gibbs内切片（SwiG）结构，我们研究了在现代GPU硬件上可将基线随机采样技术推进到何种程度。我们证明，对于LIGO和Virgo探测器观测到的典型长时长双中子星信号，在单GPU上对三个探测器网络的全部未压缩数据实现良好校准的后验推断的中位时间为12分钟，在四个设备上分片处理时降至5分钟。利用外差法压缩数据可将注入 campaign 的中位墙时间缩短至89秒，小于片段本身的时长，且能在约2分钟内对GW170817的进动自旋、潮汐波形进行推断。这使得从无信息先验状态启动、采用完整物理波形计算的随机采样技术向实时引力波参数估计推进。
英文摘要
We present a specialised GPU-native nested sampling kernel targeting rapid parameter estimation for gravitational wave inference problems. Building upon a Slice-within-Gibbs (SwiG) structure for rapid mixing, we investigate how far we can push baseline stochastic sampling techniques on modern GPU hardware. We demonstrate that for typical long-duration binary neutron star signals observed by the LIGO and Virgo detectors, we can achieve well calibrated posterior inference on the full uncompressed data of a three detector network in a median of twelve minutes on a single GPU. This falls to five minutes when sharded across four devices. Utilising heterodyning to compress the data reduces the median wall time across an injection campaign to 89 seconds -- less than the length of the segment itself -- and enables inference with precessing spin, tidal waveforms on GW170817 in around two minutes. This pushes stochastic sampling techniques using full physical waveform calculations, launched from an uninformed prior state, towards real-time gravitational wave parameter estimation.
Comments18 pages, 7 figures
