{"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/fast-and-accurate-ams-02-antiproton","title":"Fast and accurate AMS-02 antiproton likelihoods for global dark matter fits","arxiv_id":"2303.07362","date":"2023-03-13","proceeding":null,"authors":["Sowmiya Balan","Felix Kahlhoefer","Michael Korsmeier","Silvia Manconi","Kathrin Nippel"],"abstract":"The antiproton flux measurements from AMS-02 offer valuable information about the nature of dark matter, but their interpretation is complicated by large uncertainties in the modeling of cosmic ray propagation. In this work we present a novel framework to efficiently marginalise over propagation uncertainties in order to obtain robust AMS-02 likelihoods for arbitrary dark matter models. The three central ingredients of this framework are: the neural emulator DarkRayNet, which provides highly flexible predictions of the antiproton flux; the likelihood calculator pbarlike, which performs the marginalisation, taking into account the effects of solar modulation and correlations in AMS-02 data; and the global fitting framework GAMBIT, which allows for the combination of the resulting likelihood with a wide range of dark matter observables. We illustrate our approach by providing updated constraints on the annihilation cross section of WIMP dark matter into bottom quarks and by performing a state-of-the-art global fit of the scalar singlet dark matter model, including also recent results from direct detection and the LHC.","url_abs":"https://arxiv.org/abs/2303.07362v1","url_pdf":"https://arxiv.org/pdf/2303.07362v1.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":"fast-and-accurate-ams-02-antiproton","repo_url":"https://github.com/kathrinnp/DarkRayNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"fast-and-accurate-ams-02-antiproton","repo_url":"https://github.com/sowmiya-balan/pbarlike","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}