基于数据同化的大规模并行跨维度采样：应用于LISA银河系双星系统

中文摘要 AI 辅助

跨维度贝叶斯推断正成为引力波天文学中的关键组成部分。在许多相关应用中，描述数据所需的分量数量并非先验已知，必须与其参数一同推断。我们提出了一种跨维度序贯蒙特卡洛（SMC）框架，旨在利用大规模并行计算。该方法通过一系列退火分布演化粒子群体，并使用No-U-Turn采样器探索固定维参数空间，同时通过可逆跳跃的出生和死亡移动允许分量数量变化。相同的SMC构造还使得从较短数据段获得的后验样本能够在更多数据可用时进行更新，从而提高整体分析效率。我们在两个概念验证问题上验证了该方法：恢复一系列高斯脉冲以及一个简化的LISA银河系双星推断问题。在这两种情况下，该方法均能恢复注入的分量数量并产生一致的后验估计。这些结果表明，所提出的方法是迈向当前及未来引力波天文学中可扩展且并行的贝叶斯跨维度推断的一条有前景的途径。

英文摘要

Transdimensional Bayesian inference is becoming a key ingredient in gravitational-wave astronomy. In many relevant applications, the number of components needed to describe the data is not known a priori and must be inferred jointly with their parameters. We present a transdimensional Sequential Monte Carlo (SMC) framework designed to exploit massive parallelism. The method evolves a population of particles through a sequence of tempered distributions and uses the No-U-Turn Sampler for the exploration of fixed-dimensional parameter space while reversible-jump birth and death moves allow the number of components to vary. The same SMC construction also enables posterior samples obtained from a shorter data segment to be updated as additional data become available, enhancing the overall efficiency of the analysis. We validate the approach on two proof-of-concept problems: the recovery of a sequence of Gaussian pulses and a simplified LISA Galactic-binary inference problem. In both cases, the method recovers the injected number of components and produces consistent posterior estimates. These results indicate that the proposed method is a promising avenue toward scalable and parallel Bayesian transdimensional inference for current and future gravitational-wave astronomy.

发表机构

• Università degli Studi di Firenze（佛罗伦萨大学）
• INFN, Sezione di Firenze（意大利国家核物理研究所佛罗伦萨分部）
• Università di Pisa（比萨大学）
• INFN, Sezione di Pisa（意大利国家核物理研究所比萨分部）

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