论文标题
在快速轨道上：恒星流路径的快速构造
On the Fast Track: Rapid construction of stellar stream paths
论文作者
论文摘要
恒星流是银河电位的敏感探针。流动数据流数据的可能性通常是使用仿真来评估的。但是，与模拟相比，即使流道也难以量化，也很具有挑战性。在这里，我们提出了自组织图和一阶Kalman滤波器的新应用，以重建流的路径，将测量误差和数据稀疏传播到流路径不确定性中。该技术是银河模型独立的，非参数，并且在相结合的流中工作。通过这种技术，我们可以统一地分析和比较数据与模拟，从而可以对模拟技术进行比较，又可以与许多恒星流的流轨道进行比较。我们的方法在https://github.com/nstarman/trackstream上可用的公共Python软件包TrackStream中实现。
Stellar streams are sensitive probes of the Galactic potential. The likelihood of a stream model given stream data is often assessed using simulations. However, comparing to simulations is challenging when even the stream paths can be hard to quantify. Here we present a novel application of Self-Organizing Maps and first-order Kalman Filters to reconstruct a stream's path, propagating measurement errors and data sparsity into the stream path uncertainty. The technique is Galactic-model independent, non-parametric, and works on phase-wrapped streams. With this technique, we can uniformly analyze and compare data with simulations, enabling both comparison of simulation techniques and ensemble analysis with stream tracks of many stellar streams. Our method is implemented in the public Python package TrackStream, available at https://github.com/nstarman/trackstream.
