{
  "abstract": "Precision cosmology is a cornerstone of modern astronomy, and upcoming wide-field photometric surveys make it crucial to develop robust, data-driven inference methods that can handle complex survey systematics without relying on closed-form likelihoods. Simulation-Based Inference (SBI) meets this need by enabling forward simulations that encode complex survey characteristics. Previous SBI analyses in supernova cosmology have used SALT2 light-curve parameters as summary statistics; however, SALT2's two-basis spectral template imposes rigid modeling assumptions, motivating a more flexible representation. In this work, we present the first application of Functional Principal Component Analysis (FPCA) light-curve parameters as summary statistics for SBI-based cosmological inference under a non-flat ΛCDM model. On identically generated simulations, FPCA+SBI yields constraints comparable to both SALT2-based SBI and explicit-likelihood analyses, while providing more robust constraints on out-of-domain simulations than SALT2. Applying the model trained on LSST-like light curves to a spectroscopically confirmed DES Year 5 supernova sample, we recover constraints consistent with those of the DES collaboration to within 0.12 σ and 0.23 σ in Ωm and ΩΛ respectively, establishing the method's generalizability to real survey data. By introducing host-dependent systematics through a mass-dependent dust extinction law, we find FPCA-based summary statistics implicitly encode host-dependent systematics, suggesting dedicated host modeling may be unnecessary within this framework. Combined with its previously demonstrated effectiveness for photometric classification, FPCA offers a compelling foundation for unified, data-driven pipelines capable of jointly performing supernova classification and cosmological inference for upcoming wide-field photometric surveys.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.30312",
  "author_total": 2,
  "authors": [
    {
      "name": "Moonzarin Reza"
    },
    {
      "name": "Lifan Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8zsdjjme8c03p.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.30312",
  "fulltext_url": null,
  "id": "8zsdjjme8c03p",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.30312",
  "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.CO"
    ],
    [
      "arxiv",
      "astro-ph.IM"
    ],
    [
      "openalex",
      "3103|Cosmology and Gravitation Theories"
    ]
  ],
  "openalex": "W7214623520",
  "pdf_url": "https://arxiv.org/pdf/2609.30312",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8zsdjjme8c03p.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:52Z",
      "native_id": "2609.30312",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.30312"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.CO",
      "subfield": "cosmology"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "FPCA-Enhanced Simulation-Based Inference for Robust Type Ia Supernova Cosmology",
  "title_text": "FPCA-Enhanced Simulation-Based Inference for Robust Type Ia Supernova Cosmology",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:52Z",
  "url": "https://research.draconitic.com/paper/8zsdjjme8c03p",
  "venue": "arXiv",
  "versions": []
}