Search

Image Details

View Article Bookmark & Share

Choose export citation format:

Export Citation
Large resolution
Hi-Resolution Export to PDF PPT Format

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 8.

Visualizations of 14 interesting candidate groups. These candidates were initially identified based on ETI signal characteristics and subsequently passed visual inspection, a process that confirms they do not exhibit obvious RFI features. These 14 groups are part of 33 such candidates selected in this work and are equal to those previously found by Y.-C. Wang et al. (2023).

Other Images in This Article
Image in this article
Image in this article
Image in this article
Image in this article
Image in this article
Image in this article
Image in this article
Copyright and Terms & Conditions

Additional terms of reuse