CERIDWEN: Fast and Flexible GPU-Accelerated Stellar Population Inference

Abstract

JWST has increased both the number of high-redshift galaxies with high-quality spectral energy distributions (SEDs) and their information content. In parallel, wide-area surveys from Euclid, Rubin's LSST, and Roman will increase galaxy samples by orders of magnitude. Analysing these datasets requires stellar-population models that are both flexible and computationally efficient. We present CERIDWEN, a GPU-native SED fitting framework written in JAX, with an end-to-end differentiable forward model spanning stellar populations, nebular emission, dust attenuation and emission, and projection into the observer frame. Its vectorised, compiled architecture lets nested sampling replace a batch of live points in parallel on the GPU, which makes flexible stellar-population models tractable under full Bayesian inference. Automatic differentiation also provides exact gradients for the gradient-based samplers in the package. We jointly infer time-dependent chemical-enrichment histories instead of a single stellar metallicity, and demonstrate non-parametric star-formation histories (SFHs) with $\sim$120 age bins. Using $\alpha$-enhanced stellar libraries from FSPS, CERIDWEN can sample stellar [$\alpha$/Fe] jointly with [Fe/H], mass, and SFH, so that the joint posterior represents the [Fe/H]-[$\alpha$/Fe] degeneracy explicitly. In controlled mocks, CERIDWEN recovers parameters with well-calibrated posterior uncertainties, while fits to real JWST observations reproduce posteriors from the established Prospector framework: on a single GPU, CERIDWEN completes a fit in a median sampling time of $\sim$4 min, $\sim$134$\times$ faster per fit than equivalent CPU-based Prospector runs. CERIDWEN therefore makes full Bayesian inference practical for larger galaxy samples and more flexible stellar-population models, reducing computational constraints on the physical complexity explored in SED fitting.