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In this study we demonstrate that stellar masses of galaxies (Mstar) are universally correlated through a double power law function with the product of the dynamical velocities (Ve) and sizes to one-fourth power (Re^0.25) of galaxies, both measured at the effective radii. The product VeRe^0.25 represents the fourth root of the total binding energies within effective radii of galaxies. This stellar mass-binding energy correlation has an observed scatter of 0.14 dex in log(VeRe^0.25) and 0.46 dex in log(Mstar). It holds for a variety of galaxy types over a stellar mass range of nine orders of magnitude, with little evolution over cosmic time. A toy model of self-regulation between binding energies and supernovae feedback is shown to be able to reproduce the observed slopes, but the underlying physical mechanisms are still unclear. The correlation can be a potential distance estimator with an uncertainty of 0.2 dex independent of the galaxy type.
We derive the stellar-to-halo mass relation (SHMR), namely $f_starpropto M_star/M_{rm h}$ versus $M_star$ and $M_{rm h}$, for early-type galaxies from their near-IR luminosities (for $M_star$) and the position-velocity distributions of their globular
For many massive compact galaxies, their dynamical masses ($M_mathrm{dyn} propto sigma^2 r_mathrm{e}$) are lower than their stellar masses ($M_star$). We analyse the unphysical mass discrepancy $M_star / M_mathrm{dyn} > 1$ on a stellar-mass-selected
We explore the origin of stellar metallicity gradients in simulated and observed dwarf galaxies. We use FIRE-2 cosmological baryonic zoom-in simulations of 26 isolated galaxies as well as existing observational data for 10 Local Group dwarf galaxies.
We present the results of a study to determine the co-evolution of the virial and stellar masses for a sample of 83 disk galaxies between redshifts z = 0.2 - 1.2. The virial masses of these disks are computed using measured maximum rotational velocit
A significant fraction of high redshift star-forming disc galaxies are known to host giant clumps, whose nature and role in galaxy evolution are yet to be understood. In this work we first present a new method based on neural networks to detect clump