A self-consistent hydrostatic mass modelling of pressure supported dwarf galaxy Leo T (1802.04478v3)
Abstract: Assuming a hydrostatic equilibrium in an HI cloud, the joint Poisson's equation is set up and numerically solved to calculate the expected HI distribution. Unlike previous studies, the cloud is considered to be non-isothermal, and an {\it iterative} method is employed to iteratively estimate the intrinsic velocity dispersion profile using the observed second-moment of the HI data. We apply our {\it iterative} method to a recently discovered dwarf galaxy Leo T and find that its observed HI distribution does not comply with the expected one if one assumes no dark matter in it. To model the mass distribution in Leo T, we solve the Poisson's equation using a large number of trial dark matter halos and compare the model HI surface density ($\Sigma_{HI}$) profiles to the observed one to identify the best dark matter halo parameters. For Leo T, we find a pseudo-isothermal halo with core density, $\rho_0 \sim 0.67$ $\rm M_{\odot} \thinspace pc{-3}$ and core radius, $r_s \sim 37$ parsec explains the observation best. The resulting dark matter halo mass within the central 300 pc, $M_{300}$, found to be $\sim 2.7 \times 106$ $\rm M_{\odot}$. We also find that a set of dark matter halos with similar $M_{300} \sim 3.7 \times 106$ $\rm M_{\odot}$ but very different $\rho_0$ and $r_s$ values, can produce equally good $\Sigma_{HI}$ profile within the observational uncertainties. This, in turn, indicates a strong degeneracy between the halo parameters and the best fit values are not unique. Interestingly, it also implies that the mass of a dark matter halo, rather than its structure primarily directs the expected HI distribution under hydrostatic equilibrium.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.