论文标题
机器学习对宇宙距离二元性关系的预测，并具有强烈镜头的重力波事件
Machine learning forecasts of the cosmic distance duality relation with strongly lensed gravitational wave events
论文作者
论文摘要
我们使用来自爱因斯坦望远镜的模拟强烈镜头引力事件，以演示如何分别组合亮度和角直径距离，$ d_l（z）$和$ d_a（z）$，以模型的方式进行组合，以独立于与宇宙距离duality duality duality duality relation和标准的宇宙学模型进行独立的方式测试。特别是，我们使用两种机器学习方法，即遗传算法和高斯工艺，重建模拟数据，我们表明两种方法都能正确恢复基础基金模型，并在将来的爱因斯坦望远镜数据应用于中等红移时在中等红移处提供百分比级别的约束。
We use simulated strongly lensed gravitational wave events from the Einstein Telescope to demonstrate how the luminosity and angular diameter distances, $d_L(z)$ and $d_A(z)$ respectively, can be combined to test in a model independent manner for deviations from the cosmic distance duality relation and the standard cosmological model. In particular, we use two machine learning approaches, the Genetic Algorithms and Gaussian Processes, to reconstruct the mock data and we show that both approaches are capable of correctly recovering the underlying fiducial model and can provide percent-level constraints at intermediate redshifts when applied to future Einstein Telescope data.
