A story about a tipsy kangaroo: Reversible jump MCMC for model selection in the analysis of gravitational-wave signals from the coalescence of compact objects
微醺袋鼠的故事:可逆跳转MCMC在致密天体并合引力波信号分析中的模型选择
Bayesian inference is commonly employed in the analysis of gravitational-wave signals not only to estimate the source parameters, but also for model selection. The latter provides insight into the physics of the source and has the potential to inform the direction for future model development. Although model comparison is usually performed by analyzing the data separately with different models and comparing the obtained Bayesian evidences, an alternative approach consists in sampling directly over the model itself. Here, we present t-roo, a reversible jump Markov chain Monte Carlo sampler capable of performing transdimensional inference on gravitational-wave signals from compact binary coalescences. Employing t-roo, a single analysis provides simultaneously the model odds ratio and the parameter posteriors for the favored models, hence yielding a potentially substantial computational advantage, particularly when comparing many models or analyzing highly informative data. t-roo is built on the sampler eryn and is specifically designed to compare models describing different kinds of sources, i.e., binary black hole, binary neutron star, or neutron star-black hole systems, as well as multiple models for the same source class. We validate the sampler on a set of injections, finding agreement with the results obtained with the nested sampler dynesty. We then use t-roo to analyze the real events GW190425 and GW230529, for which the system's parameters alone do not provide conclusive evidence of the presence of a neutron star component. t-roo can be adapted to any model-comparison scenario, thus providing a valuable tool in particular for next-generation detectors, where analyzing data separately with competing models becomes computationally even more demanding.
展开 ▾首次将可逆跳转MCMC应用于完整CBC波形模型的跨维度比较,支持BBH/BNS/NSBH等多类波形及同类多模型的同时分析,对高信噪比数据可显著减少计算开销。
传统上,引力波信号模型选择多采用分别运行不同波形模型并比较贝叶斯证据(如 Abbott+ 2023),或使用分类参数实现同维度模型跳转。可逆跳转MCMC已在LISA数据分析等场景中实现跨维度采样 Karnesis+ 2021,但尚未用于涉及潮汐效应的不同致密双星波形模型的联合比较。本文开发的t-roo采样器通过设计质量–潮汐映射提案等机制,首次实现了BBH、BNS、NSBH模型的同时采样,可直接给出模型概率与参数后验。未来该方法可扩展至进动、偏心率及引力理论检验等复杂场景,并有望在第三代探测器的高信噪比信号分析中发挥高效筛选作用。
预印本 2026-07-23 · 收录 2026-07-24