Detecting galaxies in a large H i spectral cube

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.

Automated summary

We have developed a MPI-based PYTHON pipeline to efficiently handle the massive amount of data that will be generated for HI science. By dividing the data and using a HI source finder, we can process and filter the detections effectively. Comparison with the true source catalog reveals that detection efficiency is influenced by various factors, such as parameters.