{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/daemonflux-data-driven-muon-calibrated","title":"DAEMONFLUX: DAta-drivEn MuOn-calibrated atmospheric Neutrino Flux","arxiv_id":"2303.00022","date":"2023-02-28","proceeding":null,"authors":["Juan Pablo Yañez","Anatoli Fedynitch"],"abstract":"In this paper, we present a refined calculation of the atmospheric neutrino flux spanning from GeV to PeV energies. Our method, Daemonflux, utilizes data-driven inputs and incorporates adjustable parameters to take their uncertainties into account. By optimizing these parameters using a combination of muon data and constraints from fixed-target experiments, we achieve uncertainties in the calculated neutrino fluxes of less than 10% up to 1 TeV, with neutrino ratios constrained to below 10%. Our model performs particularly well at energies below 100 GeV, where the smallest errors are obtained. We make our model available as a software package that provides access to predictions of fluxes, ratios, and errors, including the covariance matrix obtained from the fit.","url_abs":"https://arxiv.org/abs/2303.00022v3","url_pdf":"https://arxiv.org/pdf/2303.00022v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"daemonflux-data-driven-muon-calibrated","repo_url":"https://github.com/mceq-project/daemonflux","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}