Enhanced Bayesian RFI mitigation and transient flagging using likelihood reweighting

Abstract

Abstract Contamination by radio frequency interference (RFI) is a ubiquitous challenge for radio astronomy. In particular, transient RFI is difficult to detect and avoid, especially in large data sets with many time bins. In this work, we present a Bayesian methodology for time-dependent, transient anomaly mitigation performed jointly with model fitting. The computation time for correcting transient anomalies in this manner in time-separated data sets grows proportionally with the number of time bins. We demonstrate that utilizing likelihood reweighting can allow our Bayesian anomaly mitigation method to be performed with a computation time close to independent of the number of time bins. In particular, we identify a factor of 44 improvement in computation time for a test case with 2000 time bins. We also demonstrate how this method enables the flagging threshold to be fit as a free parameter, fully automating the mitigation process. We find that this threshold fitting also prevents overcorrecting of the data in the case of wide priors. Finally, we investigate the potential of the methodology as a transient detector. We demonstrate that the method is able to reliably flag an individual anomalous data point out of 302 000 provided the Signal to Noise Ratio is $\ge\!{10}$.