On the fast track: Rapid construction of stellar stream paths
On the fast track: Rapid construction of stellar stream paths
复制标题
快车道上：快速构建恒星流路径
DOI：
10.1093/mnras/stad1166
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发表时间：
2023
影响因子：
4.8
通讯作者：
Somersalo, Erkki
中科院分区：
文献类型：
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作者：
Starkman, Nathaniel;Bovy, Jo;Webb, Jeremy J;Calvetti, Daniela;Somersalo, Erkki
恒星流是银河系潜力的敏感探测器。给定流数据的流模型的可能性通常使用模拟来评估。然而，当流路径也难以量化时，与模拟进行比较是具有挑战性的。在这里，我们提出了一种自组织映射和一阶卡尔曼滤波器的新颖应用来重建流的路径，将测量误差和数据稀疏性传播到流路径的不确定性中。该技术独立于银河模型，非参数化，适用于相位包裹流。通过这种技术，我们可以对数据与模拟进行统一分析和比较，从而实现模拟技术的比较和对许多恒星流的流轨迹的集合分析。我们的方法是在 publicPythonpackageTrackStream 中实现的，可从 https://github.com/nstarman/trackstream 获取。
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 analyse 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 publicPythonpackageTrackStream, available at https://github.com/nstarman/trackstream.