I adjust Chen & Gnedin (2022) to satellite galaxies in the local group from a suite of collisionless simulations. We validate the near-linear globular cluster mass-halo mass correlation down to Mh ~ 108 Msun, where the majority of dwarf galaxies do not host any cluster. By studying two Fornax-like satellites in the simulations, we reproduce the radial profile of globular clusters in Fornax and show that observational samples can be notably biased by incompleteness below detection limit and at large radii. See Chen & Gnedin (2023) for detais.
Figure: near-linear globular cluster mass-halo mass correlation.
The advent of the Gaia mission has enabled detailed chemical and kinematic studies of the Galactic globular clusters and revolutionized our understanding of the connections between globular cluster properties and galaxy assembly. I update the globular cluster formation model developed by Choksi & Gnedin (2019) by assigning globular clusters to particles in the IllustrisTNG simulation based on age and location. This adds spatial and kinematic information to the modeled globular clusters. The model successfully reproduces the radial distribution and various kinematic properties of the Galactic globular clusters. See Chen & Gnedin (2022) for details.
Figure: effective radii of globular cluster systems vs total mass of host halos.
Together with Hui Li and Mark Vogelsberger, I perform a suite of simulations to investigate the effects of initial density profiles on the evolution of star clusters in giant molecular clouds. We find that the uniform profile follows a "hierarchical" cluster formation mode, while the steep power-law profiles show an "accretion" dominated mode. These two cluster formation modes lead to different proprieties of the most massive clusters in giant molecular clouds. See our first paper (Chen, Li, & Vogelsberger 2021) for details.
Figure: "hierarchical" mode of cluster formation.
Based on the cosmic light speed variation, I found a novel pre-burst stage for gamma-ray bursts. I also employed a primary clustering method of machine learning to classify this stage with the data from the Fermi telescope. The work was completed in 2019 but was accepted for publication in 2021 due to the COVID-19 pandemic (see, Chen & Ma 2021).
Figure: d classification of gamma-ray burst photons that distinguishes pre-burst from main-burst.
© 2020 — Bill Chen