Gammapy: a Python Package for Gamma-Ray Astronomy

Discussion timeslot (ZOOM-Meeting): 13. July 2021 - 12:00
ZOOM-Meeting URL: https://desy.zoom.us/j/98542982538
ZOOM-Meeting ID: 98542982538
ZOOM-Meeting Passcode: ICRC2021
Corresponding Session: https://icrc2021-venue.desy.de/channel/52-Analysis-Methods-Catalogues-Community-Tools-Machine-Learning-GAD-GAI/64
Live-Stream URL: https://icrc2021-venue.desy.de/livestream/Discussion-04/5

Abstract:
'Gammapy is a community-developed, open source Python package for gamma-ray Astronomy, which is built on the scientific Python ecosystem Numpy, Scipy and Astropy. It provides methods for the analysis of gamma-ray data of many instruments including Imaging Atmospheric Cherenkov Telescopes, Water Cherenkov, as well as space based observatories.rnrnStarting from event lists and a description of the specific instrument response functions (IRF) stored in open FITS based data formats, Gammapy implements the reduction of the input data and instrument response to binned WCS, HEALPix or region based data structures. Thereby it handles the dependency of the IRFs with time, energy as well as position on the sky. It offers a variety of background estimation methods for spectral, spatial and spectro-morphological analysis. Counts, background and IRFs data are bundled in datasets and can be serialised, rebinned and stacked.rnrnGammapy supports to model binned data using Poisson maximum likelihood fitting. It comes with built-in spectral, spatial and temporal models as well as support for custom user models, to model e.g. energy dependent morphology of gamma-ray sources. Multiple datasets can be combined in a joint-likelihood approach to either handle time dependent IRFs, different classes of events or combination of data from multiple instruments. Gammapy also implements methods to estimate flux points, including likelihood profiles per energy bin, light curves as well as flux and signficance maps in energy bins.rnrnIn this contribution we present an overview of the most recent features and user interface of Gammapy along with example analyses using H.E.S.S, Fermi-LAT and simulated CTA data.'

Authors: Axel Donath | Régis Terrier
Co-Authors: Fabio Acero | Luca Giunti | Jalel Eddine Hajlaoui | Bruno Khelifi | Cosimo Nigro | Maximilian Noethe | Laura Olivera-Nieto | Fabio Pintore | Quentin Remy | José Enrique Ruiz | Atreyee Sinha | For the Gammapy collaboration
Collaboration: Gammapy

Indico-ID: 1019
Proceeding URL: https://pos.sissa.it/395/746