论文标题
使用机器学习在ALMA数据中找到隐藏的系外行星
Locating Hidden Exoplanets in ALMA Data Using Machine Learning
论文作者
论文摘要
原星磁盘中的外部球星导致分子线发射通道图中开普勒速度的局部偏差。当前表征这些偏差的方法是耗时的，并且没有统一的标准方法。我们证明机器学习可以快速，准确地检测到行星的存在。我们将模型训练从模拟产生的合成图像上，并将其应用于实际观察值，以识别实际系统中的形成行星。基于计算机视觉的机器学习方法不仅能够正确识别一个或多个行星的存在，而且还可以正确限制这些行星的位置。
Exoplanets in protoplanetary disks cause localized deviations from Keplerian velocity in channel maps of molecular line emission. Current methods of characterizing these deviations are time consuming, and there is no unified standard approach. We demonstrate that machine learning can quickly and accurately detect the presence of planets. We train our model on synthetic images generated from simulations and apply it to real observations to identify forming planets in real systems. Machine learning methods, based on computer vision, are not only capable of correctly identifying the presence of one or more planets, but they can also correctly constrain the location of those planets.
