基于解析似然模型增强的窄种群推断
Narrow Population Inference Enhanced by Analytical Likelihood Models
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中文总结 AI 辅助
该研究针对离散后验样本的种群推断方法难以约束窄种群特征的问题，提出用连续似然模型，经模拟对比验证其能更准确恢复自旋、偏心等窄分布参数，可提升致密双星系统种群推断的可靠性。
中文摘要 AI 辅助
不断增长的引力波事件目录揭示了致密双星并合性质的显著多样性。然而，基于离散后验样本的常用种群推断方法在约束窄种群特征时可能存在困难，导致种群超参数的估计出现偏差或不稳定。我们首先使用一个玩具种群模型展示了这一局限性，通过比较连续似然模型与由10³、10⁴和10⁵个样本构建的离散近似的参数恢复情况。随后，我们使用前人研究中引入的合成偏心和多源种群进行了相同的比较。尽管连续方法和离散方法的结果大致一致，但连续近似能更准确地恢复窄模拟种群的参数。特别是，两种方法得到的质量分布相似，但在自旋和偏心等窄分布参数上出现了明显差异。我们的结果表明，连续方法为自旋和偏心的推断提供了更可靠的工具，而有限样本集可能无法充分表示它们的窄种群分布。因此，连续似然模型为改进种群推断、提取关于致密双星系统形成与演化的更稳健信息提供了宝贵工具。
英文摘要
The growing catalog of gravitational-wave events has revealed substantial diversity in the properties of compact-binary mergers. However, commonly used population-inference methods based on discrete posterior samples can struggle to constrain narrow population features, resulting in biased or unstable estimates of population hyperparameters. We first demonstrate this limitation using a toy population model by comparing parameter recovery with a continuous likelihood model against discrete approximations constructed from $10^3$, $10^4$, and $10^5$ samples. We then perform the same comparison using synthetic eccentric and multisource populations introduced in previous studies. Although the continuous and discrete approaches yield broadly consistent results, the continuous approximation more accurately recovers the parameters of narrow simulated populations. In particular, while both methods produce similar mass distributions, appreciable differences arise for narrowly distributed parameters such as spin and eccentricity. Our results indicate that the continuous approach provides more reliable inference for spin and eccentricity, whose narrow population distributions can be inadequately represented by finite sample sets. Continuous likelihood models therefore offer a valuable tool for improving population inference and extracting more robust information about the formation and evolution of compact-binary systems.
