LAUSR

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