我们计划利用“银河画卷”巡天数据研究活跃的天鹅座恒星形成区中的外向流反馈。原恒星的外向流是其对母体分子云重要的反馈机制。这些反馈一方面是超声速湍动的能量来源，这种湍动会抑制恒星形成的过程；另一方面反馈会导致分子云的引力不稳定性，触发下一代的恒星形成。我们研究的主要内容包括：分子外向流的认证和统计研究、对比不同分子云中外向流反馈对分子云湍动的贡献。我们在一个试验区域进行了机器学习认证的尝试，结果显示该方法能够高效率的将研究对象扩展到整个天鹅座分子云复合体的大量数据中。通过与多波段数据的对比，我们希望对外向流反馈调节恒星形成的过程有更深入的理解。

We plan to study the outflow feedback in the active star-forming region Cygnus complex with “Milky Way Imaging Scroll Painting” survey data. Protostellar outflows are the most essential feedback mechanism on their parent clouds. Such feedback activity is responsible for the supersonic turbulence that suppresses the star forming process, and also causes gravitational instabilities that may trigger the next generation of star formation. The key topics of our project include: the identification and statistical study of molecular outflows, and estimation of energy contribution of outflow feedback processes in different molecular clouds. Identification using machine learning algorithm has been tested in a demonstration region, which shows such method can be efficiently applied to the big data set of the whole Cygnus complex. By comparing the results with multi-wavelength data, we expect to achieve a better understanding of how the outflow feedback regulates the formation of stars.