Gammapy: A Python package for gamma-ray astronomy
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Published:2023-10
Issue:
Volume:678
Page:A157
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ISSN:0004-6361
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Container-title:Astronomy & Astrophysics
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language:
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Short-container-title:A&A
Author:
Donath AxelORCID, Terrier RégisORCID, Remy QuentinORCID, Sinha AtreyeeORCID, Nigro CosimoORCID, Pintore FabioORCID, Khélifi BrunoORCID, Olivera-Nieto LauraORCID, Ruiz Jose EnriqueORCID, Brügge Kai, Linhoff MaximilianORCID, Contreras Jose Luis, Acero Fabio, Aguasca-Cabot ArnauORCID, Berge David, Bhattacharjee PoojaORCID, Buchner JohannesORCID, Boisson CatherineORCID, Carreto Fidalgo David, Chen Andrew, de Bony de Lavergne MathieuORCID, de Miranda Cardoso José Vinicius, Deil Christoph, Füßling Matthias, Funk Stefan, Giunti LucaORCID, Hinton Jim, Jouvin Léa, King Johannes, Lefaucheur Julien, Lemoine-Goumard MarianneORCID, Lenain Jean-PhilippeORCID, López-Coto RubénORCID, Mohrmann LarsORCID, Morcuende DanielORCID, Panny SebastianORCID, Regeard Maxime, Saha Lab, Siejkowski Hubert, Siemiginowska Aneta, Sipőcz Brigitta M.ORCID, Unbehaun TimORCID, van Eldik Christopher, Vuillaume ThomasORCID, Zanin Roberta
Abstract
Context. Traditionally, TeV-γ-ray astronomy has been conducted by experiments employing proprietary data and analysis software. However, the next generation of γ-ray instruments, such as the Cherenkov Telescope Array Observatory (CTAO), will be operated as open observatories. Alongside the data, they will also make the associated software tools available to a wider community. This necessity prompted the development of open, high-level, astronomical software customized for high-energy astrophysics.
Aims. In this article, we present Gammapy, an open-source Python package for the analysis of astronomical γ-ray data, and illustrate the functionalities of its first long-term-support release, version 1.0. Built on the modern Python scientific ecosystem, Gammapy provides a uniform platform for reducing and modeling data from different γ-ray instruments for many analysis scenarios. Gammapy complies with several well-established data conventions in high-energy astrophysics, providing serialized data products that are interoperable with other software packages.
Methods. Starting from event lists and instrument response functions, Gammapy provides functionalities to reduce these data by binning them in energy and sky coordinates. Several techniques for background estimation are implemented in the package to handle the residual hadronic background affecting γ-ray instruments. After the data are binned, the flux and morphology of one or more γ-ray sources can be estimated using Poisson maximum likelihood fitting and assuming a variety of spectral, temporal, and spatial models. Estimation of flux points, likelihood profiles, and light curves is also supported.
Results. After describing the structure of the package, we show, using publicly available gamma-ray data, the capabilities of Gammapy in multiple traditional and novel γ-ray analysis scenarios, such as spectral and spectro-morphological modeling and estimations of a spectral energy distribution and a light curve. Its flexibility and its power are displayed in a final multi-instrument example, where datasets from different instruments, at different stages of data reduction, are simultaneously fitted with an astrophysical flux model.
Funder
Spanish Research State Agency Institute of Cosmos Sciences Univer- sity of Barcelona European Science Cluster of Astronomy & Particle Physics ESFRI Research Infrastructures Agence Nationale de la Recherche Ramon y Cajal program Spanish Ministerio de Ciencia e Innovación NextGenerationEU and PRTR NASA
Subject
Space and Planetary Science,Astronomy and Astrophysics
Cited by
2 articles.
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