Kavli Affiliate: Roberto Maiolino
| First 5 Authors: Dingyi Zhao, Yingjie Peng, Yipeng Jing, Xiaohu Yang, Luis C. Ho
| Summary:
In $Lambda$CDM cosmology, galaxies form and evolve in their host dark matter
(DM) halos. Halo mass is crucial for understanding the halo-galaxy connection.
The abundance matching (AM) technique has been widely used to derive the halo
masses of galaxy groups. However, quenching of the central galaxy can decouple
the coevolution of its stellar mass and DM halo mass. Different halo assembly
histories can also result in significantly different final stellar mass of the
central galaxies. These processes can introduce substantial uncertainties in
the halo masses derived from the AM method, particularly leading to a
systematic bias between groups with star-forming centrals (blue groups) and
passive centrals (red groups). To improve, we developed a new machine learning
(ML) algorithm that accounts for these effects and is trained on simulations.
Our results show that the ML method eliminates the systematic bias in the
derived halo masses for blue and red groups and is, on average, $sim1/3$ more
accurate than the AM method. With careful calibration of observable quantities
from simulations and observations from SDSS, we apply our ML model to the SDSS
Yang et al. groups to derive their halo masses down to
$10^{11.5}mathrm{M_odot}$ or even lower. The derived SDSS group halo mass
function agrees well with the theoretical predictions, and the derived
stellar-to-halo mass relations for both red and blue groups matches well with
those obtained from direct weak lensing measurements. These new halo mass
estimates enable more accurate investigation of the galaxy-halo connection and
the role of the halos in galaxy evolution.
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