{
  "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 $α$-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 $\\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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.30145",
  "author_total": 3,
  "authors": [
    {
      "name": "Amanda Stoffers"
    },
    {
      "name": "Sandro Tacchella"
    },
    {
      "name": "Benjamin D. Johnson"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dsw0f1w7j7m4m.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.30145",
  "fulltext_url": null,
  "id": "dsw0f1w7j7m4m",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.30145",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.GA"
    ],
    [
      "arxiv",
      "astro-ph.IM"
    ],
    [
      "openalex",
      "3103|Galaxies: Formation, Evolution, Phenomena"
    ]
  ],
  "openalex": "W7214387268",
  "pdf_url": "https://arxiv.org/pdf/2609.30145",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-24",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dsw0f1w7j7m4m.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:11Z",
      "native_id": "2609.30145",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.30145"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.GA",
      "subfield": "galaxies"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "CERIDWEN: Fast and Flexible GPU-Accelerated Stellar Population Inference",
  "title_text": "CERIDWEN: Fast and Flexible GPU-Accelerated Stellar Population Inference",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:11Z",
  "url": "https://research.draconitic.com/paper/dsw0f1w7j7m4m",
  "venue": "arXiv",
  "versions": []
}