A comparative study of source-finding techniques in HI emission line cubes using SoFiA, MTObjects, and supervised deep learning. (arXiv:2211.12809v1 [astro-ph.IM])

The 21 cm spectral line emission of atomic neutral hydrogen (HI) is one of
the primary wavelengths observed in radio astronomy. However, the signal is
intrinsically faint and the HI content of galaxies depends on the cosmic
environment, requiring large survey volumes and survey depth to investigate the
HI Universe. As the amount of data coming from these surveys continues to
increase with technological improvements, so does the need for automatic
techniques for identifying and characterising HI sources while considering …

arxiv astro deep learning line study