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

University Of Washington, Seattle WA

Investigators

Abstract

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.