All public Github Repositories, software, tutorials relevant to my research will be added here. I will also recommend some resources I find very useful in the future. It should grow over time.
BaSC is short for Bayesian Source Characterisation, which is a software to extract point sources directly from dirty images. It saves the time of performing the deconvolution process, such as CLEAN and Maximum Entropy. More importantly, it outperforms existing source extraction packages such as the SExtractor in distinguishing two nearby sources, because BaSC's source distinguish limit is no longer confined by the size of the CLEAN beam.
Research Paper: P R Hague, H Ye, B Nikolic, S F Gull, Bayesian Source Discrimination in Radio Interferometry, MNRAS, 484, 1, 2019, 574-581
Author：Peter Hague, Haoyang Ye
A brand-new gridding function is proposed to minimise the difference between DFT and FFT dirty images during the radio interferometry imaging process. This gridding function is hence named as "least-misfit gridding function". Compared to the commonly used spheroidal function with the same window width and image cropping ratio, the least-misfit function can improve the accuracy of the FFT dirty image by at least 100 times compared to the DFT dirty image. In addition, to obtain the same accuracy that CASA achieves on the FFT dirty image, using the least-misfit gridding function instead will save a considerable amount of computational cost.
A series of Jupyter Notebook tutorials are created to help users to play with the newly proposed least-misfit gridding function. With the Python codes provided, you are also more than welcome to implement the least-misfit function to your applications and relevant research.
Research Paper: H Ye, S F Gull, S M Tan, B Nikolic, Optimal gridding and degridding in radio interferometry imaging, Preprint
Author：Sze Meng Tan