The upcoming square kilometer array (SKA) is expected to produce humongous amount of data for undertaking Hi science. We have developed an MPI-based Python pipeline to deal with the large data efficiently with the present computational resources. Our pipeline divides such large Hi 21-cm spectral cubes into several small cubelets, and then processes them in parallel using publicly available Hi source finder SoFiA- 2. The pipeline also takes care of sources at the boundaries of the cubelets and also filters out false and redundant detections. By comapring with the true source catalog, we find that the detection efficiency depends on the SoFiA- 2 parameters, such as the smoothing kernel size, linking length and threshold values. We find the optimal kernel size for all flux bins to be between 3–5 and 7–15 pixels, respectively, in the spatial and frequency directions. Comparing the recovered source parameters with the original values, we find that the output of SoFiA- 2 is highly dependent on kernel sizes and a single choice of kernel is not sufficient for all types of Hi galaxies. We also propose the use of alternative methods to SoFiA- 2, which can be used in our pipeline to find sources more robustly.

One of the major science goals of square kilometre array (SKA) is to understand the role played by atomic hydrogen (Hi) gas in the evolution of galaxies throughout cosmic time. The hyperfine transition line of the hydrogen atom at 21-cm is one of the best tools to detect and study the properties of Hi gas associated with galaxies. In this paper, we review our current understanding of Hi gas and its relationship with galaxies through observations of the 21-cm line both in emission and absorption. In addition, we provide an overviewof the Hi science that will be possible with SKA and its precursors and pathfinders, i.e., Hi 21-cm emission and absorption studies of galaxies from nearby to high redshifts that will trace various processes governing galaxy evolution.