{"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/a-bayesian-approach-to-modelling-multi","title":"A Bayesian Approach to Modelling Multi-Messenger Emission from Blazars using Lepto-Hadronic Kinetic Equations","arxiv_id":"2006.01543","date":"2020-06-02","proceeding":null,"authors":["Bruno Jiménez-Fernández","H. J. van Eerten"],"abstract":"Blazar TXS 0506+056 is the main candidate for a coincident neutrino and gamma-ray flare event. In this paper, we present a detailed kinetic lepto-hadronic emission model capable of producing a photon and neutrino spectrum given a set of parameters. Our model includes a range of large-scale geometries and both dynamical and steady-state injection models for electrons and protons. We link this model with a Markov Chain Monte Carlo sampler to obtain a powerful statistical tool that allows us to both fit the Spectral Energy Distribution and study the probability density functions and correlations of the parameters. Assuming a fiducial neutrino flux, we demonstrate how multi-messenger observations can be modelled jointly in a Bayesian framework. We find the best parameters for each of the variants of the model tested and report on their cross-correlations. Additionally, we confirm that reproducing the neutrino flux of TXS 0506+056 requires an extreme proton to electron ratio either in the local acceleration process or from external injection.","url_abs":"https://arxiv.org/abs/2006.01543v2","url_pdf":"https://arxiv.org/pdf/2006.01543v2.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":"a-bayesian-approach-to-modelling-multi","repo_url":"https://github.com/hveerten/katu","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"LGPL-3.0"}},{"paper_slug":"a-bayesian-approach-to-modelling-multi","repo_url":"https://gitlab.com/Noxbru/Katu","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"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}