Make background models

Gammapy tools to produce background models.

Make cube background models

The gammapy-make-bg-cube-models command line tool can be used to produce background cube models from the data files at a given path location.

For more details, please refer to make_bg_cube_models.

Examples

The gammapy-make-bg-cube-models tool has a few options. For a full list of options, please use:

$ gammapy-make-bg-cube-models --help

at the command line.

Command examples:

  • Create background models using a H.E.S.S. or H.E.S.S.-like dataset (can take a few minutes):

    $ gammapy-make-bg-cube-models /path/to/fits/event_lists/base/dir \
                              HESS bg_cube_models

  • Run a quick test using only a few runs:

    $ gammapy-make-bg-cube-models /path/to/fits/event_lists/base/dir \
                              HESS bg_cube_models --test

  • Create background models using the method developped in [Mayer2015] (almost equal to the default case for now):

    $ gammapy-make-bg-cube-models /path/to/fits/event_lists/base/dir \
                              HESS bg_cube_models --method michi

The output files are created in the output directory:

  • bg_observation_table.fits.gz: total observation table used for the models. The list has been filtered from observations taken at or nearby known sources.
  • bg_observation_table_grouped.fits.gz: observation table grouped according to the selected binning for the background observations.
  • bg_observation_groups.ecsv: table describing the observation groups.
  • bg_cube_model_group<ID>_<format>.fits.gz: files containing the background models for each group ID in 2 different format kinds: table, for data analysis and image for a quick visualization using eg. DS9. The table files contain also a counts (a.k.a. events) and a livetime correction data cubes.

In order to compare 2 sets of background cube models, the examples/wip_bg_cube_model_comparison.py can be used.

Datasets for testing

In order to test the background model generation tools, real data from existing experiments such as H.E.S.S. can be used. Since the data of current experiments is not public, tools to simulate datasets are described in Simulate event lists.

There is also a tool in Gammapy to simulate background cube models: make_test_bg_cube_model. It can be used to produce true background cube models to use to compare to the reconstructed ones produced with the machinery described above, using a simulated dataset using the tools from Simulate event lists. If using the same model for producing the simulated dataset and the true background cube models, the reconstructed ones produced with gammapy-make-bg-cube-models should match the true ones.

The example script wip_bg_cube_models_true_reco.py can be used to produce a true cube bg model and a reco cube bg model using the same model (except for absolute normalization). The models can be used to test the cube bg model production and can be compared to each other using the wip_bg_cube_model_comparison.py example script.

Comparing true-reco models

Two model files located in the gammapy-extra repository have been produced using the example script wip_bg_cube_models_true_reco.py:

The following plots are produced with a modified version of the wip_bg_cube_model_comparison.py example script:

TODO: remove or fix these examples

The input counts spectrum is a power-law with an index of 1.5, in order to have some counts at high energies with a reasonable amount of simulated data. In reality the background spectrum has a spectral index close to 2.7.

The bg rate appears as a spectrum of index + 1 (2.5 in this example). The reason being that, in order to produce the bg model, the contents of the cube (counts per unit time) have to be divided by the bin volume (delta x * delta y * delta E). When computing counts/delta E, the index of the bg rate increases by 1 w.r.t. the index of the power-law spectrum used to sample (or model) the counts.