标题： 快速、灵活且准确的基于机器学习的引力波马尔姆奎斯特偏差评估 显示英文标题

标题： Fast, flexible, and accurate evaluation of gravitational-wave Malmquist bias with machine learning

摘要： 许多天文学调查受限于源的亮度，引力波搜索也不例外。 从合并双星中探测引力波的可检测性受到组成致密天体的质量和自旋的影响。 为了对致密双星的分布进行无偏推断，有必要考虑这种选择效应，这被称为马尔莫奎斯特偏差。 由于来自选择效应的系统误差会随着事件数量的增加而增大，因此在未来几年内准确估计引力波天文学的观测选择函数将变得越来越重要。 我们采用密度估计方法来准确且高效地计算致密双星并合的选择函数。 我们引入了一种简单的预处理方法，显著降低了所需机器学习模型的复杂度。 我们证明，与目前最广泛使用的方法相比，我们的方法在计算成本相当的情况下具有更小的统计误差，使我们能够研究更窄的自旋幅度分布。 目前使用的方法使得$10-50\%$的有趣黑洞自旋模型无法被探测；我们的新方法可以探测$>99\%$的模型，并且对于$>80\%$的模型具有更低的不确定性。 显示英文摘要

摘要： Many astronomical surveys are limited by the brightness of the sources, and gravitational-wave searches are no exception. The detectability of gravitational waves from merging binaries is affected by the mass and spin of the constituent compact objects. To perform unbiased inference on the distribution of compact binaries, it is necessary to account for this selection effect, which is known as Malmquist bias. Since systematic error from selection effects grows with the number of events, it will be increasingly important over the coming years to accurately estimate the observational selection function for gravitational-wave astronomy. We employ density estimation methods to accurately and efficiently compute the compact binary coalescence selection function. We introduce a simple pre-processing method, which significantly reduces the complexity of the required machine learning models. We demonstrate that our method has smaller statistical errors at comparable computational cost than the method currently most widely used allowing us to probe narrower distributions of spin magnitudes. The currently used method leaves $10-50\%$ of the interesting black hole spin models inaccessible; our new method can probe $>99\%$ of the models and has a lower uncertainty for $>80\%$ of the models.