Overview¶

This page gives an overview of the main concepts in Gammapy. It is a theoretical introduction to Gammapy, explaining what data, packages, classes and methods are involved in a data analysis with Gammapy, proviging many links to other documentation pages and tutorials, but not giving code examples here. For a hands-on introduction how to use Gammapy, see Installation, Getting Started and the many Tutorials.

Gammapy is an open-source Python package for gamma-ray astronomy built on Numpy and Astropy. It is a prototype for the Cherenkov Telescope Array (CTA) science tools, and can also be used to analyse data from existing gamma-ray telescopes, if their data is available in the standard FITS format (gadf). There is good support for CTA and existing imaging atmospheric Cherenkov telescopes (IACTs, e.g. H.E.S.S., MAGIC, VERITAS), and some analysis capabilities for Fermi-LAT and HAWC and multi-mission joint likelihood analysis.

Workflow¶

To use Gammapy you need a basic knowledge of Python, Numpy, Astropy, as well as matplotlib for plotting. Many standard gamma-ray analyses can be done with few lines of configuration and code, so you can get pretty far by copy and pasting and adapting the working examples from the Gammapy documentation. But eventually, if you want to script more complex analyses, or inspect analysis results or intermediate analysis products, you need to acquire a basic to intermediate Python skill level.

To analyse data from CTA or existing IACTs, the usual workflow is to use the high-level interface in gammapy.analysis as shown in the example First analysis tutorial notebook, i.e. to write a YAML config file, and then to use AnalysisConfig and Analysis to perform the data reduction from event lists and instrument response functions (IRFs) to a reduced data format called datasets, using either 3D cube analysis or 1D region-based spectral analysis. The IACT data distributed by instruments is called “data level 3” (DL3) and is given as FITS files, as shown in the CTA with Gammapy and H.E.S.S. with Gammapy notebooks and explained in more detail in Data reduction below. Then Analysis class is then used to compute intermediate reduced analysis files like counts and exposure maps or spectra, and reduced point spread function (PSF) or energy dispersion (EDISP) information, combined in container objects called datasets (see below).

The second step is then typically to model and fit the datasets, either individually, or in a joint likelihood analysis, using the Dataset, Datasets, Fit and model classes (see Modeling and Fitting below). You can specify your model using a YAML model specification, or write Python code to specify which spectral and spatial models to use and what their rough parameters are to start the fit (such as sky position and source extension, or approximate flux level). Methods to run global model fits are available, as well as methods to compute flux points or light curves, or run simple source detection algorithms.

The analysis config file and Analysis class currently mostly scripts the data reduction up to the datasets level for the most common analysis cases. It might be extended in the future to become the “manager” or “driver” class for modeling or fitting as well, or that might remain the responsibility of the datasets, models and fit classes. Advanced users that need to run specialises analyses such as e.g. complex background modeling, or grouping of observations, have a second-level API available via dataset makers, that offer more flexibility. An example of this is shown in the Second analysis tutorial notebook.

Gammapy ships with a gammapy command line tool, that can be used to check your installation and show version information via gammapy info, to download example datasets and tutorials via gammapy download or to bootstrap an analysis by creating a default config file via gammapy analysis. To learn about the Gammapy command line tool, see gammapy.scripts.

Data reduction¶

As already mentioned in the Workflow section above, IACT analysis starts with data level 3 (DL3) FITS files consisting of event lists, instrument response information (effective area, point spread function, energy dispersion, background) and extra information concerning the observation (pointing direction, time), as well as two index tables that list the observations and declare which response should be used with which event data.

There are many data reduction options, but the main ones are whether to do a 3D cube analysis or a 1D spectral analysis, and whether to keep individual observations as separate datasets for a joint likelihood fit or whether to group and stack them. Partly background modeling choices are also already made at this data reduction stage. If you have a deep IACT observation, e.g. 100 observation runs, the data reduction can take a while. So typically you write the output datasets to file after data reduction, allowing you to read them back at any time later for modeling and fitting.

Datasets¶

The gammapy.datasets sub-package contains classes to handle reduced gamma-ray data for modeling and fitting.

The Dataset objects are the result of the data reduction step. They contain the various products (counts, exposure, energy dispersion etc) with their geometries. They also serve as the basis for modeling and fitting.

The Dataset class bundles reduced data, reduced IRFs and models. Different sub-classes support different analysis methods and fit statistics (e.g. Poisson statistics with known background or with OFF background measurements).

The Datasets are used to perform joint-likelihood fitting allowing to combine different measurements, e.g. from different observations but also from different instruments.

Modeling and Fitting¶

Beyond Dataset objects, Gammapy provides numerous functionalities related to data modeling and fitting, as well as data simulation. This includes spectral, spatial and temporal model classes to describe the gamma-ray sky. Gammapy also contains a complete API for model parameter handling and model fitting.

Time analysis¶

Light curves are represented as LightCurve objects, a wrapper class around Table. To compute light curves, use the LightCurveEstimator.

Simulation¶

Gammapy supports binned simulation, i.e. Poisson fluctuation of predicted counts maps or spectra, as well as event sampling to simulate DL3 events data. The following tutorials illustrate how to use that to predict observability, significance and sensitivity, using CTA examples: 3D map simulation, 1D spectrum simulation, and Point source sensitivity. Event sampling is demonstrated in a dedicated notebook.

Other topics¶

Gammapy is organised in sub-packages, containing many classes and functions. In this overview we only mentioned the most important concepts and parts to get started. To learn more, see the following sub packages and documentation pages: gammapy.data, gammapy.irf, gammapy.maps, gammapy.catalog, gammapy.astro, gammapy.stats, gammapy.scripts (gammapy command line tool).

Note that in Gammapy, 2D image analyses are partly done with actual 2D images that don’t have an energy axis, and partly with 3D cubes with a single energy bin, e.g. for modeling and fitting, see the 2D map analysis tutorial.

For 1D spectral modeling and fitting, Models are used, to provide uniformity within Gammapy, and to allow in future versions of Gammapy for advanced use cases where a sky region based analysis is used resulting in 1D counts spetra, but the modeling is done with a spatial model assumption, allowing for treatment of overlapping emission components, such as e.g. a gamma-ray binary with underlying emission from a pulsar wind nebula, to apply proper treatment of containment and contamination corrections. Note that the spatial model on a SkyModel is optional, you can only pass a SpectralModel, as shown in the First analysis tutorial notebook and other tutorials.

What next?¶

You now have an overview of Gammapy. We suggest you continue by tring it out, following the instructions in Installation, Getting Started and then the first and second analysis tutorials at Tutorials.

Overview Subsections