A point cloud approach to generative modeling for galaxy surveys at the field level
This work addresses the challenge of analyzing cosmological data more comprehensively by avoiding binning or summary statistics, which could benefit researchers in astrophysics and cosmology, though it appears incremental as it builds on existing diffusion models.
The authors tackled the problem of modeling galaxy distributions in cosmology by developing a diffusion-based generative model that directly handles galaxies as points in 3-D space, demonstrating its application to dark matter haloes in simulations for emulation and inference tasks.
We introduce a diffusion-based generative model to describe the distribution of galaxies in our Universe directly as a collection of points in 3-D space (coordinates) optionally with associated attributes (e.g., velocities and masses), without resorting to binning or voxelization. The custom diffusion model can be used both for emulation, reproducing essential summary statistics of the galaxy distribution, as well as inference, by computing the conditional likelihood of a galaxy field. We demonstrate a first application to massive dark matter haloes in the Quijote simulation suite. This approach can be extended to enable a comprehensive analysis of cosmological data, circumventing limitations inherent to summary statistic -- as well as neural simulation-based inference methods.