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Main Authors: Feijen, K., Terrier, R., Khélifi, B., Sinha, A., Donath, A., Mitchell, A., Remy, Q.
Format: Preprint
Published: 2025
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Online Access:https://arxiv.org/abs/2507.17622
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author Feijen, K.
Terrier, R.
Khélifi, B.
Sinha, A.
Donath, A.
Mitchell, A.
Remy, Q.
author_facet Feijen, K.
Terrier, R.
Khélifi, B.
Sinha, A.
Donath, A.
Mitchell, A.
Remy, Q.
contents 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. 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. 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. 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 HESSJ1825-137, 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.
format Preprint
id arxiv_https___arxiv_org_abs_2507_17622
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Energy-dependent gamma-ray morphology estimation tool in Gammapy
Feijen, K.
Terrier, R.
Khélifi, B.
Sinha, A.
Donath, A.
Mitchell, A.
Remy, Q.
High Energy Astrophysical Phenomena
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. 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. 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. 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 HESSJ1825-137, 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.
title Energy-dependent gamma-ray morphology estimation tool in Gammapy
topic High Energy Astrophysical Phenomena
url https://arxiv.org/abs/2507.17622