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: 2228990
• 负责人: Bryna Hazelton
• 金额: $ 27.2万
• 依托单位: University of Washington
• 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=2228990&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.

随着无线电发射器数量的增加，卫星星座与地面天文学的共存是一个日益严重的挑战。对天文学家的极端宇宙学信号是21 cm的“自旋翻转”过渡，表明宇宙中存在中性氢。该信号以1420 MHz发射，但由于宇宙红移而从非常遥远的星系中接收到一系列较低的频率。在人类生成的无线通信中，检测到这种弱信号的干扰可能很困难。该研究项目将使用机器学习算法更好地检测和减轻这种干扰，这将使在早期宇宙中检测中性氢。本科生将参与该计划的各个方面，为他们提供在频谱管理，太空情境意识和机器学习算法的关键问题方面的动手经验。卫星星座的射频干扰（RFI）对旨在检测宇宙时间中中性氢的21 cm信号的观察射电天文学实验构成了关键威胁。这些高度敏感的实验必须在一千小时内整合以检测红移的21 cm信号。即使是非常微弱的RFI，在这些极敏感的特性上也成为一种显着的污染物。目前，没有单个RFI检测技术可以有效地识别非常明亮和非常微弱的RFI（信号强度的差异可能会差异多达六个数量级）。该研究团队将开发一个弱监督的机器学习框架，该框架使用现有的RFI检测技术来创建适用于所有事件的自洽的标志策略，从降低的发射器到立方体的陆地信号的微弱反射。这奖反映了NSF的宣传任务，并通过对基础的智力效果进行评估，并通过评估诚实地进行了评估。