An Improved Machine Learning Approach for Radio Frequency Interference Mitigation in FAST–SETI Survey Archival Data
Year: 2026
Type: article
Abstract: The search for extraterrestrial intelligence (SETI) commensal surveys aim to scan the sky to detect technosignatures from extraterrestrial life. A major challenge in SETI is the effective mitigation of radio frequency interference (RFI), a critical step that is particularly vital for the highly sensitive Five-hundred-meter Aperture Spherical radio Telescope (FAST). While initial RFI mitigation (e.g., removal of persistent and drifting narrowband RFI) are essential, residual RFI often persists, posing significant challenges due to its complex and various nature. In this paper, we propose and apply an improved machine learning approach, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, to identify and mitigate residual RFI in FAST–SETI commensal survey archival data from 2019 July. After initial RFI mitigation, we successfully identify and remove 36,977 residual RFIs (accounting for ∼77.87%) within approximately 1.678 s using the DBSCAN algorithm. This result shows that we have achieved a 7.44% higher removal rate than previous machine learning methods, along with a 24.85% reduction in execution time. We finally find interesting candidate signals consistent with previous studies, and retain one candidate signal following further analysis. Therefore, DBSCAN algorithm can mitigate more residual RFI with higher computational efficiency while preserving the candidate signals that we are interested in.
Source: The Astronomical Journal
Authors: Li-Li Zhao, Xiao-Hang Luan, Xin Chao, Yu-Chen Wang, Jian-Kang Li, Zhen-Zhao Tao, Tong-Jie Zhang, Hong-Feng Wang, Dan Werthimer
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,499
OpenAlex ID: W4417531550