LeHaMoC: a versatile time-dependent lepto-hadronic modeling code for high-energy astrophysical sources
S. I. Stathopoulos, M. Petropoulou, G. Vasilopoulos, A. Mastichiadis

TL;DR
LeHaMoC is a fast, versatile, time-dependent leptohadronic modeling code designed for high-energy astrophysical sources, capable of fitting multi-messenger data and simulating neutrino and electromagnetic signals.
Contribution
It introduces a new, efficient numerical code for modeling time-dependent leptohadronic processes in astrophysical sources, with validation against existing tools and applications to real data.
Findings
Good agreement with existing code within 10-30%
Successfully fitted AGN spectral energy distribution
Simulated neutrino and electromagnetic signals from NGC 1068
Abstract
Recent associations of high-energy neutrinos with active galactic nuclei (AGN) have revived the interest in leptohadronic models of radiation from astrophysical sources. The rapid increase in the amount of acquired multi-messenger data will require soon fast numerical models that may be applied to large source samples. We develop a time-dependent leptohadronic code, LeHaMoC, that offers several notable benefits compared to other existing codes, such as versatility and speed. LeHaMoC solves the Fokker-Planck equations of photons and relativistic particles (i.e. electrons, positrons, protons, and neutrinos) produced in a homogeneous magnetized source that may also be expanding. The code utilizes a fully implicit difference scheme that allows fast computation of steady-state and dynamically evolving physical problems. We first present test cases where we compare the numerical results…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsParticle physics theoretical and experimental studies · Astrophysics and Cosmic Phenomena · Computational Physics and Python Applications
