arXiv:2605.29016v1 Announce Type: cross Abstract: We investigate conditional diffusion modeling for three-dimensional 21 cm lightcone emulation, focusing on cubes with a sky-plane size of $64\times64$ and a line-of-sight depth up to 1024 cells. Relative to earlier 2D studies, the 3D setting is substantially harder because memory limits enforce very small micro-batches while the underlying voxel distribution is highly skewed and long tailed. We perform controlled comparisons across preprocessing choices, dynamic-range compression settings, architecture depth, and training duration using $25{,}6
The continuous advancements in AI, particularly diffusion models, are enabling more complex simulations and data processing, pushing the boundaries of scientific research.
This development allows for improved and faster emulation of cosmological phenomena, which is crucial for understanding the early universe and training-data generation for future astrophysical discoveries.
The ability to generate high-fidelity 3D cosmological data using AI could accelerate research that previously required extensive observational data or computationally expensive simulations, making sophisticated analysis more accessible.
- · Cosmological researchers
- · AI model developers
- · Astrophysics community
- · Supercomputing facilities (for initial model training)
More efficient generation of synthetic cosmological data for scientific analysis.
Reduced reliance on extremely large-scale, costly telescope observations for some types of cosmological studies, allowing for more targeted use of those resources.
Potential for new discoveries in cosmology by enabling faster iteration on hypotheses and exploration of a broader parameter space than currently possible.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG