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 ∼120 age bins. Using α-enhanced stellar libraries from FSPS, CERIDWEN can sample stellar [α/Fe] jointly with [Fe/H], mass, and SFH, so that the joint posterior represents the [Fe/H]-[α/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 ∼4 min, ∼134× 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.
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BibTeX
@misc{stoffers2026ceridwen,
title = {{CERIDWEN: Fast and Flexible GPU-Accelerated Stellar Population Inference}},
author = {Amanda Stoffers and Sandro Tacchella and Benjamin D. Johnson},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.30145},
eprint = {2609.30145},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.30145},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - CERIDWEN: Fast and Flexible GPU-Accelerated Stellar Population Inference
AU - Amanda Stoffers
AU - Sandro Tacchella
AU - Benjamin D. Johnson
PY - 2026
DA - 2026/09/24
PB - arXiv
DO - 10.48550/arxiv.2609.30145
UR - https://arxiv.org/abs/2609.30145
AB - 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 ∼120 age bins. Using α-enhanced stellar libraries from FSPS, CERIDWEN can sample stellar [α/Fe] jointly with [Fe/H], mass, and SFH, so that the joint posterior represents the [Fe/H]-[α/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 ∼4 min, ∼134× 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.
ER -