论文标题
通过机器学习，快速，灵活且准确评估引力波恶质偏差
Fast, flexible, and accurate evaluation of gravitational-wave Malmquist bias with machine learning
论文作者
论文摘要
许多天文学调查受源的亮度限制，重力波搜索也不例外。重力与合并二进制的可检测性受组成品紧凑型物体的质量和自旋影响。为了对紧凑型二进制文件的分布进行公正的推断，有必要考虑这种选择效应，这被称为Malmquist偏差。由于选择效果的系统误差随事件的数量而增长，因此在未来几年中，准确估计引力波天文学的观察性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有效地计算紧凑的二元合并选择函数。我们介绍了一种简单的预处理方法，该方法大大降低了所需的机器学习模型的复杂性。我们证明，与当前最广泛使用的方法相比，我们的方法的统计误差较小，允许我们探测自旋幅度的分布。当前使用的方法留下了$ 10-50 \％$ $ $ $ $ $的黑洞旋转模型无法访问；我们的新方法可以探测模型的$> 99 \％$，并且对于$> 80 \％$ $的$> 80 \％$的不确定性较低。
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.
