论文标题
使用高斯过程的旋转曲线分解：考虑到数据相关性导致无偏见的结果
Rotation curve decompositions with Gaussian Processes: taking into account data correlations leads to unbiased results
论文作者
论文摘要
拟合动力学模型时，通常会忽略磁盘星系旋转曲线中速度测量之间的相关性。在这里，我展示了如何使用高斯过程中的旋转曲线分解中考虑数据相关性。我发现，在相关参数上边缘化对于获得星系中发光和暗物质分布的无偏估计而言至关重要。
Correlations between velocity measurements in disk galaxy rotation curves are usually neglected when fitting dynamical models. Here I show how data correlations can be taken into account in rotation curve decompositions using Gaussian Processes. I find that marginalizing over correlation parameters proves critical to obtain unbiased estimates of the luminous and dark matter distributions in galaxies.
