set_bkg_model

Context: modeling

Synopsis

Set the background model expression for a PHA data set.

Syntax

set_bkg_model(id, model=None, bkg_id=None)

Description

The function is also called set_bkg_source().

The background emission can be fit by a model, defined by the `set_bkg_model` call, rather than subtracted from the data. If the background is subtracted then the background model is ignored when fitting the data.

Examples

Example 1

The background is model by a gaussian line ( gauss1d model component called bline ) together with an absorbed polynomial (the bgnd component). 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)

Example 2

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 scale component).

>>> bmodel = xsphabs.gabs * powlaw1d.pl
>>> set_bkg_model(2, bmodel)
>>> set_bkg_model(2, bmodel * const1d.scale, bkg_id=2)

PARAMETERS

The parameters for this function are:

Parameter	Type information	Definition
id	int or str, optional	The data set containing the source expression. If not given then the default identifier is used, as returned by `get_default_id` .
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.
bkg_id	int, str, or None, optional	The identifier for the background of the data set, in cases where multiple backgrounds are provided.

Notes

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 `model` parameter. If given two un-named arguments, then they are interpreted as the `id` and `model` parameters, respectively.

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 src_model and bkg_model represent the source and background model expressions set by calls to `set_model` and `set_bkg_model` respectively, the source data is fit by:

src_model + scale * bkg_model

where scale is 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_model` should be used instead.

Bugs

See the bugs pages on the Sherpa website for an up-to-date listing of known bugs.