{
  "abstract": "Quantifying the information content in multi-wavelength observations is critical for setting exposure times for upcoming X-ray and 21-cm HI radio surveys. We train convolutional neural networks (CNNs) on mock observations of halos from the IllustrisTNG100 and TNG300 simulations, combining data from soft X-ray channels from a CCD or a microcalorimeter with HI intensity, velocity, and dispersion maps, to infer halo mass, gas fractions, metallicity, and [O/Fe] abundance. Multi-band (X-ray and HI) combinations consistently outperform single-band inference for gas fractions. X-ray outperforms HI observations for measuring halo mass, but both bands contribute similarly when measuring the cool (T<10^5 K) gas fraction in halos with significant cool gas content. Using matched exposure times, a micro-calorimeter improves metallicity inference over the CCD by a factor of 1.75, enabling precise measurements of [O/Fe] alpha-enhancement for the largest halos. The larger volume of TNG300 allows inference of group halo masses, finding an inference RMSE of 0.04 dex with a 100 ksec X-ray exposure time. These results demonstrate how deep learning can evaluate strategies for developing instruments and designing surveys for these expensive observations targeting gaseous halos.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.16421",
  "author_total": 6,
  "authors": [
    {
      "name": "Kalvyn N. Poncelet Adams"
    },
    {
      "name": "Benjamin D. Oppenheimer"
    },
    {
      "name": "Naomi Gluck"
    },
    {
      "name": "Caleb Ogle"
    },
    {
      "name": "Matthew Ho"
    },
    {
      "name": "Daisuke Nagai"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/awsm1ec1fwz07.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.16421",
  "fulltext_url": null,
  "id": "awsm1ec1fwz07",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.16421",
  "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.GA"
    ],
    [
      "openalex",
      "3103|Galaxies: Formation, Evolution, Phenomena"
    ]
  ],
  "openalex": "W7213426286",
  "pdf_url": "https://arxiv.org/pdf/2609.16421",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/awsm1ec1fwz07.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:41Z",
      "native_id": "2609.16421",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.16421"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.GA",
      "subfield": "galaxies"
    }
  ],
  "title": "CNN-Based Inference of Gaseous Halo Properties from Synthetic X-ray and 21-cm HI Observations",
  "title_text": "CNN-Based Inference of Gaseous Halo Properties from Synthetic X-ray and 21-cm HI Observations",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:41Z",
  "url": "https://research.draconitic.com/paper/awsm1ec1fwz07",
  "venue": "arXiv",
  "versions": []
}