Kavli Affiliate: Mark Vogelsberger
| First 5 Authors: Tri Nguyen, Francisco Villaescusa-Navarro, Siddharth Mishra-Sharma, Carolina Cuesta-Lazaro, Paul Torrey
| Summary:
The connection between galaxies and their host dark matter (DM) halos is
critical to our understanding of cosmology, galaxy formation, and DM physics.
To maximize the return of upcoming cosmological surveys, we need an accurate
way to model this complex relationship. Many techniques have been developed to
model this connection, from Halo Occupation Distribution (HOD) to empirical and
semi-analytic models to hydrodynamic. Hydrodynamic simulations can incorporate
more detailed astrophysical processes but are computationally expensive; HODs,
on the other hand, are computationally cheap but have limited accuracy. In this
work, we present NeHOD, a generative framework based on variational diffusion
model and Transformer, for painting galaxies/subhalos on top of DM with an
accuracy of hydrodynamic simulations but at a computational cost similar to
HOD. By modeling galaxies/subhalos as point clouds, instead of binning or
voxelization, we can resolve small spatial scales down to the resolution of the
simulations. For each halo, NeHOD predicts the positions, velocities, masses,
and concentrations of its central and satellite galaxies. We train NeHOD on the
TNG-Warm DM suite of the DREAMS project, which consists of 1024 high-resolution
zoom-in hydrodynamic simulations of Milky Way-mass halos with varying warm DM
mass and astrophysical parameters. We show that our model captures the complex
relationships between subhalo properties as a function of the simulation
parameters, including the mass functions, stellar-halo mass relations,
concentration-mass relations, and spatial clustering. Our method can be used
for a large variety of downstream applications, from galaxy clustering to
strong lensing studies.
| Search Query: ArXiv Query: search_query=au:”Mark Vogelsberger”&id_list=&start=0&max_results=3