Energy-dependent gamma-ray morphology estimation tool in Gammapy

¹ Université Paris Cité, CNRS, Astroparticule et Cosmologie, 75013 Paris, France
² Tata Institute of Fundamental Research, 1 Homi Bhabha Road, Colaba, Mumbai 400 005, India
³ Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA, USA
⁴ Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen Centre for Astroparticle Physics, Nikolaus-Fiebiger-Str. 2, 91058 Erlangen, Germany
⁵ Max-Planck-Institut für Kernphysik, PO Box 103980, 69029 Heidelberg, Germany

^★ Corresponding author: kirsty.feijen@gmail.com

Received: 18 April 2025
Accepted: 22 July 2025

Abstract

Context. An understanding of the energy dependence of gamma-ray sources can yield important information on the underlying emission mechanisms. However, despite the detection of energy-dependent morphologies in many TeV sources, we lack a proper quantification of such measurements.

Aims. We introduce an estimation tool within the Gammapy landscape, an open-source Python package for the analysis of gamma-ray data, for quantifying the energy-dependent morphology of a gamma-ray source.

Methods. The proposed method fits the spatial morphology in a global fit across all energy slices (null hypothesis) and compares this to separate fits for each energy slice (alternative hypothesis). These are modelled using forward-folding methods, and the significance of the variability is quantified by comparing the test statistics of the two hypotheses.

Results. We present a general tool for probing changes in the spatial morphology with energy, employing a full forward-folding approach with a 3D likelihood. We present its usage on a real dataset from H.E.S.S. and on a simulated dataset to quantify the significance of the energy dependence for sources of different sizes. In the first example, which utilises a subset of data from HESS J1825–137, we observe extended emission at lower energies that becomes more compact at higher energies. The tool indicates a very significant variability (9.8σ) in the case of the largely extended emission. In the second example, a source with a smaller extent (~0.1°), simulated using the CTAO response, shows the tool can still provide a statistically significant variation (9.7σ) on small scales.