Vol 20, No 8

Radio frequency interference mitigation using pseudoinverse learning autoencoders

Hong-Feng Wang, Mao Yuan, Qian Yin, Ping Guo, Wei-Wei Zhu, Di Li, Si-Bo Feng


Abstract Radio frequency interference (RFI) is an important challenge in radio astronomy. RFI comes from various sources and increasingly impacts astronomical observation as telescopes become more sensitive. In this study, we propose a fast and effective method for removing RFI in pulsar data. We use pseudo-inverse learning to train a single hidden layer auto-encoder (AE). We demonstrate that the AE can quickly learn the RFI signatures and then remove them from fast-sampled spectra, leaving real pulsar signals. This method has the advantage over traditional threshold-based filter method in that it does not completely remove contaminated channels, which could also contain useful astronomical information.


Keywords pulsars: general — methods: numerical — methods: data analysis

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