Toward a Complete Census of Galactic Supernova Remnants

一颗大质量恒星以超新星的剧烈爆炸结束了它的生命。在一次超新星爆炸后，留下的碎片发光。这种碎片被称为超新星残骸(SNR)。与超新星的短暂闪光相比，SNR的持续时间要长得多，甚至几千年。研究SNR提供了一个窗口，让我们了解银河系中大质量恒星的数量以及它们在生命结束时注入的能量。尽管它们很重要，但对银河系SNR的普查严重不完整。这种不完整是由于难以在银河系混乱的部分定位银河系SNR，以及缺乏敏感数据造成的。研究人员将使用新的数据集来创建整个银河系的新SNR目录。他们将扩大“西弗吉尼亚Spot”计划，该计划训练大学STEM专业的学生向公立学校的孩子们进行科学演讲，然后支持这些演讲。在这个项目中，他们将在SNR上开发一个新的专题介绍。这项研究计划基于美国国家科学基金会甚大阵列(VLA)射电望远镜的观测，结合天基红外观测，并通过来自南非猫鼬望远镜的新射电连续谱数据进一步加强。SNR在无线电波长很亮，可以利用辅助数据与其他类型的物体区分开来。研究人员将使用机器学习来验证确定的候选对象，将已知SNR的外观与SNR候选对象的外观进行比较。根据之前的搜索和一个测试场的结果，这次搜索预计将使已知的SNR人口翻一番。这将把预期和已知的SNR群体拉近距离，并将为后续研究提供丰富的新对象。该项目由天文学和天体物理研究资助计划和既定的刺激竞争研究计划(EPSCoR)联合资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

A massive star ends its life by exploding violently as a supernova. After a supernova explosion, the debris left behind glows. This debris is called a supernova remnant (SNR). Compared to the short-duration flash of a supernova, SNRs last much longer, many thousands of years. Studying SNRs provides a window into the population of massive stars in a galaxy and the energy injected by them at the end of their lives. Despite their importance, the census of SNRs in the Milky Way is severely incomplete. This incompleteness is caused by difficulties locating Galactic SNRs in confusing parts of the Galaxy and a lack of sensitive data. The investigators will use new data sets to create a catalog of new SNRs across the Milky Way. They will expand the “West Virginia SPOT” program, which trains college STEM majors to give scientific presentations to public school children and then supports these presentations. In this program, they will develop a new feature SPOT presentation on SNRs. This research program is based on observations with the NSF’s Very Large Array (VLA) Radio Telescope, combined with space-based infrared observations and further enhanced by new radio continuum data from the South African MeerKAT telescope. SNRs are bright at radio wavelengths and can be distinguished from other types of objects using ancillary data. The researchers will verify the identified candidates using machine-learning, comparing the appearance of known SNRs with that of the SNR candidates. Based on previous searches and the results from a test field, this search is expected to double the known SNR population. This would bring the expected and known SNR populations close together and would provide a wealth of new objects for follow up studies.This project is jointly funded by the Astronomy and Astrophysics Research Grants Program and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.