{
  "abstract": "Weak gravitational lensing maps compactly encode the evolution of cosmic large-scale structure and are a key tool for cosmological analyses. Performing inference directly at the map level allows flexible choices of statistics and can increase constraining power. Conventional methods rely solely on N-body simulations and are computationally expensive. Generative machine-learning emulators can accelerate map-level theory prediction. However, existing GAN-based map-level surrogates still have limited statistical fidelity. They can produce over-smoothed maps, may fail to capture the full distribution of generated map sets and can be difficult to train. Continuous normalizing flows trained with flow matching have recently emerged as a powerful class of generative models. We present a residual label-conditional flow matching generative network that conditions explicitly on the matter density $Ω_m$ and clustering amplitude $σ_8$ for a fixed source redshift distribution $n(z)$. The model learns a continuous probability flow in a residual space from label-specific noise distributions to convergence maps. We evaluate it using pixel and peak statistics, the power spectrum, bispectrum, power-spectrum correlation matrices, and other validation metrics. Compared with the previous GAN benchmark, the proposed method improves the typical fidelity of generated maps from below 10% and below 20% to below 1% and below 5% for basic and higher-order statistics, respectively. The agreement at the level of map distributions is also very good: maps generated from random noise match well the distribution of maps generated with N-body simulations from random initial conditions. This work brings us closer to a practical mass-map emulator that captures the cosmological signal while supporting multiple forms of data analysis.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.23114",
  "author_total": 2,
  "authors": [
    {
      "name": "Guangjian Li"
    },
    {
      "name": "Tomasz Kacprzak"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6cb350961cxt2.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.23114",
  "fulltext_url": null,
  "id": "6cb350961cxt2",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.23114",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.CO"
    ],
    [
      "openalex",
      "3103|Cosmology and Gravitation Theories"
    ]
  ],
  "openalex": "W7162330589",
  "pdf_url": "https://arxiv.org/pdf/2605.23114",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6cb350961cxt2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:50Z",
      "native_id": "2605.23114",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.23114"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.CO",
      "subfield": "cosmology"
    }
  ],
  "title": "Increasing the Precision of Surrogate Models for Weak Lensing Mass Maps with Flow Matching",
  "title_text": "Increasing the Precision of Surrogate Models for Weak Lensing Mass Maps with Flow Matching",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:50Z",
  "url": "https://research.draconitic.com/paper/6cb350961cxt2",
  "venue": "arXiv",
  "versions": []
}