Glossary and references


1D Analysis

1D analysis or spectral analysis where data are reduced to a simple 1D geometry along the reconstructed energy axis. In Cherenkov astronomy, this is classically performed with a OFF background measurement.

3D Analysis

3D analysis or cube analysis, where data are reduced to a 3D cube with spatial coordinates and energy axes. In gammapy, these cube are represented by Map objects (see maps - Sky maps) and contained in a MapDataset object.


Short for effective area: it is the IRF representing the detector collection area. See Effective area.


The cash statistic is a Poisson fit statistic usually used when signal and background can be modeled. It is defined as \(2 \times log(L)\) See Cash : Poisson data with background model in fit statistics.


In Gammapy a dataset bundles the data, IRFs, model and a likelihood function. Based on the model and IRFs the predicted number of counts are computed and compared to the measured counts using the likelihood.


Short for energy dispersion: it is the IRF that represents the probability of measuring a given reconstructed energy as a function of the true photon energy. See Energy Dispersion


Short for “field of view”: it indicates the angular aperture (sometimes also the solid angle) on the sky that is visible by the instrument with a single pointing.


Short for Good Time Interval: it indicates a continuous time interval of data acquisition. In CTA, it also represents a time interval in which the IRFs are supposed to be constant.


Short for Instrument Response Function. They are used to model the probability to detect a photon with a number of measured characteristics. See IRF Theory and irf - Instrument response functions.

Joint Analysis

A joint fit across multiple datasets implies that each dataset is handled independently during the data reduction stage, and the statistics combined during the likelihood fit. The likelihood is computed for each dataset and summed to get the total fit statistic. See Joint Analysis


Short for Mission Elapsed Time; see also Mission elapsed times (MET) in Time handling in Gammapy.

Reco Energy

The reconstructed (or measured) energy (often written e_reco) is the energy of the measured photon by contrast with its actual true energy. Measured quantities such as counts are represented along a reco energy axis.

Reflected Background

Background estimation method typically used for spectral analysis.

Ring Background

Background estimation method typically used for image analysis.


Short for “region of interest”: it indicates the spatial region in which the data are analyzed. In practice, at each energy it corresponds with the sky region in which the dataset mask is True.

Stacked Analysis

In a stacked analysis individual observations are reduced to datasets which are then stacked to produce a single reduced dataset. The latter is then used to obtain physical information through model fitting. Some approximations must be made to perform dataset stacking (e.g. loss of individual background normalization, averaging of instrument responses, loss of information outside region of interest etc), but this can reduce very significantly the computing and memory cost. For details, see Stacking Multiple Datasets

True Energy

The true energy (often written e_true) is the energy of the incident photon by contrast with the energy reconstructed by the instrument. Instrument response functions are represented along a true energy axis.


The WStat is a Poisson fit statistic usually used for ON-OFF analysis. It is based on the profile likelihood method where the unknown background parameters are marginalized. See WStat : Poisson data with background measurement in fit statistics.


This is the bibliography containing the literature references for the implemented methods referenced from the Gammapy docs.


Albert et al. (2007), “Unfolding of differential energy spectra in the MAGIC experiment”,


Berge et al. (2007), “Background modelling in very-high-energy gamma-ray astronomy”


Cash (1979), “Parameter estimation in astronomy through application of the likelihood ratio”


Cousins et al. (2007), “Evaluation of three methods for calculating statistical significance when incorporating a systematic uncertainty into a test of the background-only hypothesis for a Poisson process”


Feldman & Cousins (1998), “Unified approach to the classical statistical analysis of small signals”


Lafferty & Wyatt (1994), “Where to stick your data points: The treatment of measurements within wide bins”


Li & Ma (1983), “Analysis methods for results in gamma-ray astronomy”


Meyer et al. (2010), “The Crab Nebula as a standard candle in very high-energy astrophysics”


de Naurois (2012), “Very High Energy astronomy from H.E.S.S. to CTA. Opening of a new astronomical window on the non-thermal Universe”,


Piron et al. (2001), “Temporal and spectral gamma-ray properties of Mkn 421 above 250 GeV from CAT observations between 1996 and 2000”,


Rolke et al. (2005), “Limits and confidence intervals in the presence of nuisance parameters”,


Stewart (2009), “Maximum-likelihood detection of sources among Poissonian noise”


H.E.S.S. Collaboration (2018), “The H.E.S.S. Galactic plane survey”


Mohrmann et al. (2019), “Validation of open-source science tools and background model construction in γ-ray astronomy”

Software references:


Raue (2012), “PyFACT: Python and FITS analysis for Cherenkov telescopes”


Robitaille et al. (2013) “Astropy: A community Python package for astronomy”


Knödlseder et at. (2016) “GammaLib and ctools. A software framework for the analysis of astronomical gamma-ray data”


Fermi LAT Collaboration (2013) “Science Tools: LAT Data Analysis Tools”


Michael Mayer (2015) “Pulsar wind nebulae at high energies”

Other gamma-ray packages

Here are some other software packages for gamma-ray astronomy:

  • Gammalib /ctools is a C++ package with Python wrapper, similar to the Fermi-LAT ScienceTools, that to a large degree uses the same input data formats as Gammapy.

  • 3ML is a Python package that uses existing packages (e.g. the Fermi-LAT ScienceTools or the HAWC software) to deal with the data and IRFs and compute the likelihood for a given model.

  • Sherpa — X-ray modeling and fitting package by the Chandra X-ray Center

  • ctapipe — CTA Python pipeline experimental version

  • FermiPy — Fermi-LAT science tools high level Python interface by Matthew Wood

  • gammatools — Python tools for Fermi-LAT gamma-ray data analysis by Matthew Wood

  • pointlike – Fermi-LAT science tools alternative by Toby Burnett

  • naima — an SED modeling and fitting package by Victor Zabalza

  • Gamera — a C++ gamma-ray source modeling package (SED, SNR model, Galactic population model) with a Python wrapper called Gappa by Joachim Hahn

  • FLaapLUC — Fermi/LAT automatic aperture photometry light-curve pipeline by Jean-Philippe Lenain

  • — prototype web app for CTA data access / analysis, not open source.

  • act-analysis — Python scripts and Makefiles for some common gamma-ray data analysis tasks by Karl Kosack

  • VHEObserverTools — tools to predict detectability at VHE by Jeremy Perkins

  • photon_simulator — Python code to simulate X-ray observations

  • pycrflux — Python module to plot cosmic-ray flux

  • Andy strong has C++ codes (GALPROP and Galplot) for Galactic cosmic rays and emission and source population synthesis at .

Other useful packages

In addition to the packages mentioned in the last section and at Dependencies, here’s a few other Python packages you might find useful / interesting: