秦婓，现任韩国高等研究院(KIAS)博士后研究员，2020至2023年任韩国天文与空间研究院(KASI)博士后，2020年博士毕业于西澳大利亚大学(UWA)。现为DESI、SKA以及WALLABY等国际大型巡天项目成员。韩国天文学会成员，韩国人工智能天文学会成员。主要从事宇宙大尺度结构、星系与暗物质晕的联系、人工智能天文学等方面的研究。The next generation of galaxy surveys will provide more precise measurements of galaxy clustering than have previously been possible. The 21-cm radio signals that are emitted from  neutral atomic hydrogen (HI) gas will be detected by large-area radio surveys such as WALLABY and the SKA, and deliver galaxy positions and velocities that can be used to measure galaxy clustering statistics. But, to harness this information to improve our cosmological understanding, and learn about the physics of dark matter and dark energy, we need to accurately model the manner in which galaxies detected in HI trace the underlying matter distribution of the Universe. For this purpose, we develop a new HI-based Halo Occupation Distribution (HOD)  model, which makes predictions for the number of galaxies present in dark matter halos conditional on their HI mass.  The parameterised HOD model is fit and validated using the DarkSage semi-analytic model, where we show that the HOD parameters can be modelled by simple linear and quadratic functions of HI mass. As such, this work enables -- for the first time -- a simple prescription for placing galaxies of different HI mass within dark matter halos in a way that is able to reproduce the HI mass-dependent galaxy clustering and HI mass function simultaneously and without requiring knowledge of the optical properties of the galaxies. We also present a new   galaxy number density profile of haloes, which makes predictions for the positions of all galaxies (rather than HI galaxies only) in the host halo, different to the widely adopted Navarro–Frenk–White (NFW) profile, since galaxies tend to be found more in the outskirts of halos (nearer the virial radius) than an NFW profile. We also derive the analytic expressions for the circular velocity and gravitational potential energy for this profile model.