Toward a Complete Census of Galactic Supernova Remnants

一颗巨大的星星以超新星的形式剧烈爆炸而结束了自己的生命。 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，SNRs持续的时间要长得多，长达数千年。研究SNRs提供了一个窗口，可以了解星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。尽管它们的重要性，银河系中SNRs的普查是严重不完整的。 这种不完整性是由于难以在银河系混乱的部分定位银河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银河系新SNR的目录。 他们将扩大“西弗吉尼亚SPOT”计划，该计划培训大学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。在这个计划中，他们将开发一个新的功能SPOT介绍信噪比。这项研究计划的基础是美国国家科学基金会的甚大阵列射电望远镜的观测，结合天基红外观测，并通过南非MeerKAT望远镜的新射电连续谱数据得到进一步加强。 SNR在无线电波段是明亮的，可以使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已识别的候选者，将已知SNR的外观与SNR候选者的外观进行比较。 根据之前的搜索和测试场的结果，预计此次搜索将使已知SNR总体增加一倍。 这将使预期的和已知的SNR群体接近，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赠款计划和刺激竞争性研究的既定计划（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知识价值和更广泛的影响进行评估，被认为值得支持审查标准。

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.