{"paper":{"title":"Gammapy - A prototype for the CTA science tools","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"Arache Djannati-Ata\\\"i, Arjun Voruganti, Axel Donath, Brigitta Sipocz, Bruno Kh\\'elifi, Catherine Boisson, Christoph Deil, Cyril Trichard, Dirk Lennarz, Ellis Owen, Fabio Acero, Jason Watson, Jean-Philippe Lenain, Johannes King, Jos\\'e Enrique Ruiz, Julien Lefaucheur, Lars Mohrmann, L\\'ea Jouvin, Manuel Paz Arribas, Marion Spir-Jacob, Matteo Cerruti, Matthew Wood, Nachiketa Chakraborty, R\\'egis Terrier, Roberta Zanin, Rub\\'en L\\'opez Coto, Santiago Pita, Stefan Klepser, Thomas Vuillaume (for the CTA Consortium), Zeljka Bosnjak","submitted_at":"2017-09-06T09:58:51Z","abstract_excerpt":"Gammapy is a Python package for high-level gamma-ray data analysis built on Numpy, Scipy and Astropy. It enables us to analyze gamma-ray data and to create sky images, spectra and lightcurves, from event lists and instrument response information, and to determine the position, morphology and spectra of gamma-ray sources. So far Gammapy has mostly been used to analyze data from H.E.S.S. and Fermi-LAT, and is now being used for the simulation and analysis of observations from the Cherenkov Telescope Array (CTA). We have proposed Gammapy as a prototype for the CTA science tools. This contribution"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.01751","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1709.01751/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}