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

随着无线电发射机数量的增加，卫星星座与地面天文学共存是一个日益严峻的挑战。对天文学家来说，一个极其重要的宇宙学信号是21厘米的“自旋翻转”转变，这表明宇宙中存在中性氢。这一信号以1420兆赫的频率发射，但由于宇宙红移，从遥远的星系接收到的频率较低。在存在来自用于无线通信的人为射频传输的干扰的情况下，检测该微弱信号可能是困难的。这个研究项目将使用机器学习算法来更好地检测和缓解这种干扰，这将使在非常早期的宇宙中检测到中性氢成为可能。本科生将参与这个项目的所有方面，为他们提供在频谱管理、空间态势感知和机器学习算法等关键问题上的实践经验。来自卫星星座的射频干扰(RFI)对试图探测跨宇宙时间的21厘米中性氢信号的观测射电天文学实验构成了严重威胁。这些高度敏感的实验必须积分超过1000个小时才能检测到红移21厘米的信号；在这些极端敏感的情况下，即使非常微弱的RFI也会成为严重的污染物。目前，没有一种单一的RFI检测技术可以有效地识别非常亮和非常弱的RFI(这两种RFI在信号强度上可能相差多达六个数量级)。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检测技术来创建一种适用于所有事件的自洽标记策略，从明亮的发射器到立方体卫星上微弱的地面信号反射。这一奖项反映了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。

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.