Performance evaluation of baseline-dependent window functions with several weighing functions

Abstract

Radio interferometric data volume is exponentially increasing with the potential to cause slow processing and data storage issues for radio observations recorded at high time and frequency resolutions. This necessitates that a sort of data compression is imposed. The conventional method to compress the data is averaging across time and frequency. However, this results in amplitude loss and source distortion at the edges of the field of view. To reduce amplitude loss and source distortion, baseline-dependent window functions (BDWFs) are proposed in theliterature. BDWFs are visibility data compression methods using window functions to retainthe signals within a field of interest (FoI) and to suppress signals outside this FoI. However,BDWFs are used with window functions as discussed in the signal processing field without any optimisation. This thesis evaluates the performance of BDWFs and then proposes to use machine learning with gradient descent to optimize the window functions employed in BDWFs. Results show that the convergence of the objective function is limited due to the band-limited nature of the window functions in the Fourier space. BDWFs performance is also investigated and discussed using several weighting schemes. Results show that there exists an optimal parameter tuning (not necessarily unique) that suggests an optimal combination of BDWFs and density sampling. With this, ∼ 4 % smearing is observed within the FoI, and ∼ 80 % source suppression is achieved outside the FoI using the MeerKAT telescope at 1.4 GHz, sampled at 1 s and 184.3 kHz then averaged with BDWFs to achieve a compression factor of 4 in time and 3 in frequency.