Toward a Complete Census of Galactic Supernova Remnants

一颗大质量恒星会以超新星的形式剧烈爆炸而结束生命。超新星爆炸后，留下的碎片会发光。这些碎片被称为超新星遗迹（SNR）。与超新星的短暂闪光相比，信噪比持续的时间要长得多，长达数千年。研究信噪比为了解星系中大质量恒星的数量以及它们在生命结束时所注入的能量提供了一个窗口。尽管它们很重要，但对银河系中信噪比的普查仍然严重不完整。这种不完整性是由于难以在银河系令人困惑的部分定位银河系信噪比和缺乏敏感数据造成的。研究人员将使用新的数据集创建一个横跨银河系的新信噪比目录。他们将扩大“西弗吉尼亚SPOT”项目，该项目培训大学STEM专业的学生向公立学校的孩子们做科学演讲，然后为这些演讲提供支持。在这个项目中，他们将开发一个关于信噪比的新功能SPOT演示。这项研究计划是基于美国国家科学基金会的甚大阵列（VLA）射电望远镜的观测，结合天基红外观测，并进一步加强了来自南非MeerKAT望远镜的新的射电连续体数据。信噪比在无线电波长上是明亮的，可以通过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，将已知信噪比的外观与候选信噪比的外观进行比较。根据之前的搜索和试验场的结果，该搜索预计将使已知的信噪比增加一倍。这将使预期的和已知的信噪比种群接近在一起，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学研究资助计划和促进竞争性研究的既定计划（EPSCoR）共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.