AI 中文总结
研究针对引力波信号分析中的模型选择问题，提出可逆跳跃马尔可夫链蒙特卡罗采样器t-roo，能同时给出模型优势比和参数后验，基于eryn构建，可比较多种源模型，经验证及实际事件分析，为下一代探测器提供了有价值的模型比较工具。
AI 中文摘要
贝叶斯推断常用于引力波信号分析，不仅用于估计源参数，还用于模型选择，后者有助于洞察源物理并指导未来模型发展。通常通过用不同模型分别分析数据并比较贝叶斯证据来进行模型比较，另一种方法是直接对模型本身进行采样。本文提出t-roo，一种可逆跳跃马尔可夫链蒙特卡罗采样器，能对致密双星合并的引力波信号进行跨维推断。使用t-roo，单次分析可同时提供模型优势比和优选模型的参数后验，具有计算优势。t-roo基于eryn采样器构建，可比较不同类型源的模型。在一组注入数据上验证了该采样器，与嵌套采样器dynesty结果一致。还用t-roo分析了真实事件GW190425和GW230529，其系统参数无法确定是否存在中子星成分。t-roo可适应任何模型比较场景，为下一代探测器提供了有价值的工具。
英文摘要
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.
