Prospects for combined analyses of hadronic emission from $\gamma$-ray sources in the Milky Way with CTA and KM3NeT
T. Unbehaun, L. Mohrmann, S. Funk (authors of the CTA Consortium), S., Aiello, A. Albert, S. Alves Garre, Z. Aly, A. Ambrosone, F. Ameli, M. Andre,, E. Androutsou, M. Anghinolfi, M. Anguita, L. Aphecetche, M. Ardid, S. Ardid,, H. Atmani, J. Aublin, C. Bagatelas, L. Bailly-Salins

TL;DR
This paper evaluates the potential of combined gamma-ray and neutrino data analysis from CTA and KM3NeT to better understand hadronic emission in Galactic sources, demonstrating improved modeling capabilities and constraints.
Contribution
It introduces a combined analysis framework using Gammapy for gamma-ray and neutrino data, enhancing the ability to distinguish hadronic from leptonic emission in astrophysical sources.
Findings
Combined analysis improves modeling of gamma-ray and neutrino emission.
Constraints on hadronic contribution are below 15% for the best source.
Demonstrates Gammapy can process neutrino data alongside gamma-ray data.
Abstract
The Cherenkov Telescope Array and the KM3NeT neutrino telescopes are major upcoming facilities in the fields of -ray and neutrino astronomy, respectively. Possible simultaneous production of rays and neutrinos in astrophysical accelerators of cosmic-ray nuclei motivates a combination of their data. We assess the potential of a combined analysis of CTA and KM3NeT data to determine the contribution of hadronic emission processes in known Galactic -ray emitters, comparing this result to the cases of two separate analyses. In doing so, we demonstrate the capability of Gammapy, an open-source software package for the analysis of -ray data, to also process data from neutrino telescopes. For a selection of prototypical -ray sources within our Galaxy, we obtain models for primary proton and electron spectra in the hadronic and leptonic emission scenario,…
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.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
