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Contamination by Radio Frequency Interference (RFI) is a ubiquitous challengefor radio astronomy. In particular, transient RFI is difficult to detect andavoid, especially in large data sets with many time bins. In this work, wepresent a Bayesian methodology for time-dependent, transient anomalymitigation. In general, the computation time for correcting for transientanomalies in time-separated data sets grows proportionally with the number oftime bins. We demonstrate that utilising likelihood reweighting can allow ourBayesian anomaly mitigation method to be performed with a computation timeclose to independent of the number of time bins. In particular, we identify afactor of 25 improvement in computation time for a test case with 2000 timebins. We also demonstrate how this method enables the flagging threshold to befit for as a free parameter, fully automating the mitigation process. We findthat this threshold fitting also prevents overcorrecting of the data in thecase of wide priors. Finally, we investigate the potential of the methodologyas a transient detector. We demonstrate that the method is able to reliablyflag an individual anomalous data point out of 302,000 provided the SNR > 10.

Enhanced Bayesian RFI Mitigation and Transient Flagging Using Likelihood Reweighting