http://arxiv.org/abs/2301.04188
Radio interferometry calibration and Radio Frequency Interference (RFI) removal are usually done separately. Here we show that jointly modelling the antenna gains and RFI has significant benefits when the RFI follows precise trajectories, such as for satellites. One surprising benefit is improved calibration solutions, by leveraging the RFI signal itself. We present TABASCAL (TrAjectory BAsed RFI Subtraction and CALibration), a new algorithm that jointly models the RFI signal & trajectory as well as the calibration parameters in post-correlation visibilities. TABASCAL can use either optimisation or fully Bayesian statistical methods to find calibration solutions in contaminated data that would otherwise be thrown away. We test TABASCAL on simulated MeerKAT calibration observations contaminated by satellite-based RFI with amplitudes varying between -20 dB and 15 dB relative to a 1 Jy source. We obtain gain estimates that are both unbiased and up to an order of magnitude better constrained compared to the case of no RFI. TABASCAL can be further applied to an adjacent target observation: using 5 minutes of calibration data resulted in an image with about half the noise compared to using purely flagged data, and only 23% higher than an uncontaminated observation. The source detection threshold and recovered flux distribution of TABASCAL-processed data was on par with uncontaminated data. In contrast, RFI flagging alone resulted in consistent underestimation of source fluxes and less sources detected. For a mean RFI amplitude of 17 Jy, using TABASCAL leads to less than 1% loss of data compared to 75% data loss from ideal $3\sigma$ flagging, a significant increase in data available for science analysis. Although we have examined the case of satellite RFI, TABASCAL should work for any RFI moving on parameterizable trajectories, such as planes or objects fixed to the ground.
C. Finlay, B. Bassett, M. Kunz, et. al.
Thu, 12 Jan 23
27/68
Comments: 19 pages, 16 figures, 4 tables