{
  "abstract": "We present a neural network emulator for the bns_nurates library, which computes neutrino reaction rates used in binary neutron star merger simulations. Following a hybrid strategy, we emulate only the computationally dominant two-dimensional reactions---pair processes, bremsstrahlung, and inelastic neutrino-electron scattering---while evaluating the inexpensive one-dimensional processes still with bns_nurates. We find that the emulator reproduces the opacities to within a few percent across the relevant thermodynamic conditions and accelerates their evaluation by roughly an order of magnitude. When deployed in a global Athenak binary neutron star merger simulation, it recovers the remnant structure, neutrino luminosities, and ejecta composition of a reference run using the full bns_nurates library, at approximately half the overall cost",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.28740",
  "author_total": 4,
  "authors": [
    {
      "name": "Yi Qiu"
    },
    {
      "name": "David Radice"
    },
    {
      "name": "Raffaele Ferrari"
    },
    {
      "name": "Albino Perego"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7qj0jgc7bzsn3.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.28740",
  "fulltext_url": null,
  "id": "7qj0jgc7bzsn3",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.28740",
  "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.HE"
    ],
    [
      "openalex",
      "3103|Pulsars and Gravitational Waves Research"
    ]
  ],
  "openalex": "W7223218781",
  "pdf_url": "https://arxiv.org/pdf/2610.28740",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7qj0jgc7bzsn3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:19Z",
      "native_id": "2610.28740",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.28740"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.HE",
      "subfield": "high-energy-astrophysics"
    }
  ],
  "title": "A Hybrid Neural Network Emulator for M1 Neutrino Reaction Rates in Binary Neutron Star Merger Simulations",
  "title_text": "A Hybrid Neural Network Emulator for M1 Neutrino Reaction Rates in Binary Neutron Star Merger Simulations",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:19Z",
  "url": "https://research.draconitic.com/paper/7qj0jgc7bzsn3",
  "venue": "arXiv",
  "versions": []
}