References

Publications

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”

Knoedlseder2012

Knödlseder et at. (2012) “GammaLib: A New 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”

Glossary

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

  • http://voparis-cta-client.obspm.fr/ — 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 http://www.mpe.mpg.de/~aws/propagate.html .

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: