Speaker
Description
Fuzzy dark matter (FDM) provides an alternative to the standard cold dark matter picture by suppressing small-scale structure formation through quantum pressure effects. In this work, we test whether FDM can better reproduce the high neutral hydrogen fractions inferred from recent JWST observations at redshifts z > 8. We generate 21 cm reionization histories with 21cmFirstCLASS over a grid of FDM masses and fractions, and incorporate JWST $x_{HI}$ constraints using Bayesian inference that preserves their full non-Gaussian uncertainty distributions. To compare simulations with observations, we use a hybrid neural-network framework that combines convolutional layers for spatial features with recurrent layers for redshift evolution. We find that models near $m_{FDM}$ ~ $10^{-22}$ eV and $f_{FDM}$ ~ 0.04 provide the best agreement with the current data, while lighter masses are more strongly constrained. These results suggest that FDM can delay early structure formation and produce a later, more gradual reionization history, in better agreement with current high-redshift observations.