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An Improved Machine Learning Approach for Radio Frequency Interference Mitigation in FAST–SETI Survey Archival Data

  • 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

Li-Li Zhao et al 2026 The Astronomical Journal 171 .

  • Provider: AAS Journals

Caption: Figure 5.

Evaluation of processing speed for residual RFI removal, comparing DBSCAN (light blue) and KNN (dark blue) algorithms. The histogram shows the distribution of execution times across 20 identical runs. DBSCAN demonstrates significantly faster processing, with trials clustering in the 1.6–1.8 s range, while KNN trials predominantly fall in the 2.1–2.3 s range, representing an average speed improvement of approximately 24.85%.

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