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. Here we show the addition of a PowerLawNormSpectralModel
in which the norm and tilt parameters are unfrozen as an example.
from gammapy.makers import MapDatasetMaker, FoVBackgroundMaker, SafeMaskMaker
from gammapy.datasets import MapDataset
from gammapy.data import DataStore
from gammapy.modeling.models import PowerLawNormSpectralModel
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)
spectral_model = PowerLawNormSpectralModel()
spectral_model.norm.frozen = False
spectral_model.tilt.frozen = False
fov_bkg_maker = FoVBackgroundMaker(
method="fit",
exclusion_mask=exclusion_mask,
spectral_model=spectral_model
)
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)
It is also possible to implement other normed models, such as the
PiecewiseNormSpectralModel. To do so,
you can utilise most of the above code with an adaption to the spectral_model applied
in the fov_bkg_maker.
from gammapy.modeling.models import PiecewiseNormSpectralModel
spectral_model = PiecewiseNormSpectralModel(
energy=energy_axis.edges,
norms=np.ones_like(energy_axis.edges)
)
fov_bkg_maker = FoVBackgroundMaker(
method="fit",
exclusion_mask=exclusion_mask,
spectral_model=spectral_model
)
Note: to prevent poorly constrained norm parameters or large variance in
the last bins, the binning should be adjusted to have wider bins at higher energies.
This ensures there are enough statistics per bin, enabling the fit to converge.
