- sherpa.astro.ui.set_bkg_model(id, model=None, bkg_id=None)
Set the background model expression for a PHA data set.
The background emission can be fit by a model, defined by the
set_bkg_modelcall, rather than subtracted from the data. If the background is subtracted then the background model is ignored when fitting the data.
model (str or sherpa.models.Model object) – This defines the model used to fit the data. It can be a Python expression or a string version of it.
Delete the model expression from a data set.
Fit one or more data sets.
Integrate 1D source expressions.
Set the model expression for a data set.
Define the convolved background model expression for a PHA data set.
Display the background model expression for a data set.
The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the
modelparameter. If given two un-named arguments, then they are interpreted as the
The emission defined by the background model expression is included in the fit to the source dataset, scaling by exposure time and area size (given by the ratio of the background to source BACKSCAL values). That is, if
bkg_modelrepresent the source and background model expressions set by calls to
set_bkg_modelrespectively, the source data is fit by:
src_model + scale * bkg_model
scaleis the scaling factor.
PHA data sets will automatically apply the instrumental response (ARF and RMF) to the background expression. For some cases this is not useful - for example, when different responses should be applied to different model components - in which case
set_bkg_full_modelshould be used instead.
The background is model by a gaussian line (
gauss1dmodel component called
bline) together with an absorbed polynomial (the
bgndcomponent). The absorbing component (
gal) is also used in the source expression.
>>> set_model(xsphabs.gal*powlaw1d.pl) >>> set_bkg_model(gauss1d.bline + gal*polynom1d.bgnd)
In this example, the default data set has two background estimates, so models are set for both components. The same model is applied to both, except that the relative normalisations are allowed to vary (by inclusion of the
>>> bmodel = xsphabs.gabs * powlaw1d.pl >>> set_bkg_model(2, bmodel) >>> set_bkg_model(2, bmodel * const1d.scale, bkg_id=2)