Speaker
Description
The Epoch of Reionization marks the period when the first galaxies ionized the neutral hydrogen in the intergalactic medium. Upcoming 21 cm experiment such as SKA, together with line-intensity mapping and galaxy surveys, will observe overlapping cosmic volumes. Much of their constraining power will come from cross-correlating different tracers, since each observable is affected by different foregrounds and systematics. However, cross-correlation analyses require maps, while many existing reionization emulators predict only summary statistics such as power spectra.
We train a conditional Diffusion Transformer as a fast generative surrogate for the 21cmFAST forward model within its training prior. The model generates four physically coupled three-dimensional fields: 21 cm brightness temperature, neutral fraction, dark matter density, and line-of-sight velocity. These fields provide the map-level ingredients needed for 21 cm statistics, reionization morphology, redshift-space effects, and cross-correlations with other tracers. The model is conditioned on astrophysical and cosmological parameters, as well as redshift over ($5 \leq z \leq 25$). By learning the joint distribution of these fields, the surrogate captures not only the individual field statistics, but also the physical correlations between them.
We evaluate the generated fields using physical statistics that are not directly optimized during training. The resulting surrogate turns expensive reionization simulations into fast, field-level generative forward models. This enables efficient parameter sweeps, population studies, and map-level multi-tracer analyses, and provides a route toward simulation-based inference with physically coupled reionization observables.