FoV background#
Overview#
Background models stored in IRF might not predict accurately the actual number of background counts.
To correct the predicted counts, one can use the data themselves in regions deprived of gamma-ray signal.
The field-of-view background technique is used to adjust the predicted counts on the measured ones outside
an exclusion mask. This technique is recommended for 3D analysis, in particular when stacking Datasets.
Gammapy provides the FoVBackgroundMaker. The latter creates a
FoVBackgroundModel which combines the background predicted number of counts
and a NormSpectralModel which allows to renormalize the background cube, and
possibly to change its spectral distribution. By default, only the norm parameter of a
PowerLawNormSpectralModel is left free. If needed the spectral parameters
can be unfrozen.
from gammapy.makers import MapDatasetMaker, FoVBackgroundMaker, SafeMaskMaker
from gammapy.datasets import MapDataset
from gammapy.data import DataStore
from gammapy.maps import MapAxis, WcsGeom, Map
from regions import CircleSkyRegion
from astropy import units as u
data_store = DataStore.from_dir("$GAMMAPY_DATA/hess-dl3-dr1")
observations = data_store.get_observations([23592, 23559])
energy_axis = MapAxis.from_energy_bounds("0.5 TeV", "10 TeV", nbin=5)
energy_axis_true = MapAxis.from_energy_bounds("0.3 TeV", "20 TeV", nbin=20, name="energy_true")
geom = WcsGeom.create(skydir=(83.63, 22.01), axes=[energy_axis], width=5, binsz=0.02)
stacked = MapDataset.create(geom, energy_axis_true=energy_axis_true)
maker = MapDatasetMaker()
safe_mask_maker = SafeMaskMaker(
methods=["aeff-default", "offset-max"], offset_max="2.5 deg"
)
circle = CircleSkyRegion(center=geom.center_skydir, radius=0.2 * u.deg)
exclusion_mask = geom.region_mask([circle], inside=False)
fov_bkg_maker = FoVBackgroundMaker(method="fit", exclusion_mask=exclusion_mask)
for obs in observations:
dataset = maker.run(stacked, obs)
dataset = safe_mask_maker.run(dataset, obs)
dataset = fov_bkg_maker.run(dataset)
stacked.stack(dataset)
