DarkMatterFM-Registry: A Machine-Readable Registry of Dark-Matter Models Extracted from the arXiv Literature

Abstract

To use a dark-matter model one needs to know what defines it, which observables it affects, which codes and likelihoods evaluate it, and under what assumptions a published bound applies. That information is scattered across tens of thousands of papers. We present DarkMatterFM-Registry, a machine-readable registry of dark-matter models extracted from arXiv full texts by a staged large-language-model (LLM) pipeline: 6,145 structured model cards, validated against a seven-file JSON Schema suite, ranked by expected value for observational follow-up, and enriched for the top ~2,000 with observables, constraints, and theory-to-observable routes. Schema checks pass 97.8% of cards, and an LLM-judged accuracy study on a stratified card sample finds 81% of section verdicts correct, 18% partially correct, and none hallucinated. As a first application we turn the registry into a priority-weighted roadmap of 25 concrete projects the community could build. The roadmap is built by judging every computational step of every enriched model's theory-to-observable routes as available, partial, or missing, and aggregating the resulting typed gaps weighted by model priority. We find that what blocks the most high-priority models is not missing theory but missing shared infrastructure: model files for existing relic-abundance solvers, a general non-cold Boltzmann-code framework, reusable indirect-detection likelihoods, and a Lyman-$α$ forest likelihood for non-cold dark matter (the largest gap for structure-formation probes). The model cards, schemas, roadmap, and ranking are released making the project reproducible and the cards available for the community.