Collaborative Research: SWIFT-SAT: RFI Detection Across Six Orders of Magnitude in Intensity: A Unifying Framework with Weakly Supervised Machine Learning

合作研究：SWIFT-SAT：强度六个数量级的 RFI 检测：弱监督机器学习的统一框架

• 批准号: 2228989
• 负责人: Stephen Bach
• 金额: $ 47万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2228989&HistoricalAwards=false
• 关键词: Collaborative; Research; SWIFT; SAT; RFI

The coexistence of satellite constellations with ground-based astronomy is a growing challenge with the increase in the number of radio transmitters. One cosmological signal of extreme importance to astronomers is the 21 cm “spin flip” transition, indicating the presence of neutral hydrogen in the cosmos. This signal is emitted at 1420 MHz but received at a range of lower frequencies from very distant galaxies due to cosmological redshift. Detecting this weak signal can be difficult in the presence of interference from human-generated radio-frequency transmissions for wireless communications. This research project will use machine learning algorithms to better detect and mitigate such interference, which will enable detection of neutral hydrogen in the very early universe. Undergraduate students will participate in all aspects of this program, providing them with hands-on experience in key issues of spectrum management, space situational awareness, and machine learning algorithms. Radio frequency interference (RFI) from satellite constellations poses a critical threat to observational radio astronomy experiments seeking to detect the 21 cm signal of neutral hydrogen across cosmic time. These highly sensitive experiments must integrate over a thousand hours to detect the redshifted 21 cm signal; even very faint RFI becomes a significant contaminant at these extreme sensitivities. Currently, no single RFI detection technique can effectively identify both very bright and very faint RFI (which can differ by as much as six orders of magnitude in signal strength). This research team will develop a weakly supervised machine learning framework that uses existing RFI detection techniques to create a self-consistent flagging strategy suitable for all events, from bright transmitters down to faint reflections of terrestrial signals off CubeSats.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.

随着无线电发射机数量的增加，卫星星座与地面天文学的共存面临着越来越大的挑战。对天文学家来说极其重要的一个宇宙学信号是 21 厘米的“自旋翻转”跃迁，它表明卫星中存在中性氢。该信号以 1420 MHz 发射，但由于宇宙学红移而以较低频率从非常遥远的星系接收。在存在人类产生的无线通信射频传输干扰的情况下，该研究项目将使用机器学习算法来更好地检测和减轻这种干扰，这将使本科生参与检测早期宇宙中的中性氢。在该计划的各个方面，为他们提供频谱管理、空间态势感知和机器学习算法等关键问题的实践经验，这对寻求解决这一问题的射电天文学观测实验构成了严重威胁。检测21厘米这些高度敏感的实验必须整合超过 1000 个小时才能检测到红移的 21 厘米信号；在如此高的灵敏度下，即使是非常微弱的 RFI 也会成为重要的污染物。非常亮和非常微弱的 RFI（信号强度可能相差六个数量级） 该研究团队将开发一个弱监督机器学习框架，该框架使用现有的 RFI 检测技术来创建一个自洽的 RFI。适用于所有事件的标记策略，从明亮的发射机到立方体卫星地面信号的微弱反射。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。