Energy-dependent gamma-ray morphology estimation tool in Gammapy - Feijen, K.; Terrier, R.; Khélifi, B.; Sinha, A.; Donath, A.; Mitchell, Alison; Remy, Q. (Astronomy & Astrophysics, 2025)
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 (9.8I) 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 (9.7I) on small scales.
CRIS-Team Scopus Importer:2025-09-12
Keywords: Gamma rays: general; Methods: data analysis