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

The best reference to TeV data analysis is Chapter 7 of Mathieu de Naurois’s habilitation thesis.

[Albert2007]Albert et al. (2007), “Unfolding of differential energy spectra in the MAGIC experiment”,
[Berge2007]Berge et al. (2007), “Background modelling in very-high-energy gamma-ray astronomy”
[Cash1979]Cash (1979), “Parameter estimation in astronomy through application of the likelihood ratio”
[Cousins2007]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”
[Feldman1998]Feldman & Cousins (1998), “Unified approach to the classical statistical analysis of small signals”
[Lafferty1994]Lafferty & Wyatt (1994), “Where to stick your data points: The treatment of measurements within wide bins”
[LiMa1983]Li & Ma (1983), “Analysis methods for results in gamma-ray astronomy”
[Meyer2010]Meyer et al. (2010), “The Crab Nebula as a standard candle in very high-energy astrophysics”
[Naurois2012]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”,
[Piron2001]Piron et al. (2001), “Temporal and spectral gamma-ray properties of Mkn 421 above 250 GeV from CAT observations between 1996 and 2000”,
[Rolke2005]Rolke et al. (2005), “Limits and confidence intervals in the presence of nuisance parameters”,
[Stewart2009]Stewart (2009), “Maximum-likelihood detection of sources among Poissonian noise”

Software references:

[Raue2012]Raue (2012), “PyFACT: Python and FITS analysis for Cherenkov telescopes”
[Robitaille2013]Robitaille et al. (2013) “Astropy: A community Python package for astronomy”
[Knoedlseder2016]Knödlseder et at. (2016) “GammaLib and ctools. A software framework for the analysis of astronomical gamma-ray data”
[FSSC2013]Fermi LAT Collaboration (2013) “Science Tools: LAT Data Analysis Tools”
[Mayer2015]Michael Mayer (2015) “Pulsar wind nebulae at high energies”


[MET]mission elapsed time; see also Mission elapsed times (MET) in Time handling in Gammapy.

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 C<->Urve 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 Gammapy Dependencies, here’s a few other Python packages you might find useful / interesting: