Defines a model expression to be used for the background.
The command BG is an abbreviated equivalent.
sherpa> BACKGROUND [<dataset range> | ALLSETS [ID]] = <modelExpr>
<dataset range> = # (or more generally #:#,#:#, etc.) such that #
specifies a dataset number and #:# represents an inclusive range of
datasets; one may specify multiple inclusive ranges by separating them
with commas. The default dataset is dataset 1. The ID modifier is used
if and only if the Sherpa state object variable multiback is set to 1,
i.e., if more than one background dataset is to be associated with a
single source dataset. The ID modifier may be any unreserved string
(e.g., A, foo, etc.), i.e., a string that is not a parsable command.
The model expression,
<modelExpr>,
is an algebraic combination of one or more of the following elements:
{<sherpa_modelname> | <sherpa_modelname>[modelname] |
<modelname> | <model_stack> | <nested_model>}
along with numerical values. The following operators are
recognized: + - * / ( ) { }.
See the CREATE command for further information.
Note that:
-
The documentation on
Sherpa Models contains a
summary list, and descriptions, of the models
that are available within Sherpa, which include models
from XSPEC, v. 11.3.
-
By default, if the model expression includes a model
component that has not previously been established, Sherpa
will prompt for the initial parameter values for that model component.
This prompting can be turned off using the
PARAMPROMPT OFF command.
-
In CIAO 3.1 the definition of INSTRUMENT BACK is required for both
filtering and fitting of PHA data if either background file or background
models have been defined. INSTRUMENT BACK is set automatically
when the PHA source file is input to Sherpa, however it
is deleted if the NEW background file is input for a given data set,
thus the new INSTRUMENT BACK has to defined on the command line
before filtering and fitting the data with the new background file.
To reset a background model stack, issue the command:
sherpa> BACKGROUND [<dataset range> | ALLSETS] =
How the background model stack is used depends upon whether or not the
the source data have been background-subtracted:
-
If the source data are background-subtracted, then the background model
stack is applied only to the fit of the background data themselves.
The resulting statistic is added to that from the source model fit;
changing a background model parameter has no effect on the source fit.
-
If the source data are not background-subtracted, then the background model
stack is evaluated twice, once on a grid appropriate for the background data
and a second time on a grid appropriate to the source data.
To the latter array of amplitudes is added
the evaluated source model amplitudes. Thus changing a background model
parameter affects the fit statistic in both the background fit and the
source fit!
Note on Model Normalization. Because the background is,
by definition, an "extended object," the normalization of a best-fit
background model will be affected by the size of the background
extraction region, which is proportional to the area of the sky from
which the photons came. In particular, if the areas of the source
and background extraction regions differ, then the normalization may not
be easily interpretable: which region does it correspond to? In CIAO 3.0,
the rules are the following:
- If source and background data have both been input, the best-fit
background normalization corresponds to the source extraction region,
with one exception noted below.
- If background data only have been input, the best-fit
background normalization corresponds to the background extraction region.
- If source and background data have both been input, but the
ratio of extraction "areas" differs as a function of energy
(as it can for, e.g., XMM grating data),
then the normalization corresponds to the pixel area equivalent
to a BACKSCAL of one.
Define a model to be used for the background and set background model
parameter values:
sherpa> DATA 2 data.dat
sherpa> BACKGROUND 2 = GAUSS
GAUSS.fwhm parameter value [10]
GAUSS.pos parameter value [0] 3
GAUSS.ampl parameter value [1] 2:1:10
This command defines the Sherpa model GAUSS as the background model for dataset
number 2. The user accepted the given initial guessed value for
the parameter fwhm (using the <RETURN> key),
entered a value of 3 for parameter pos, and entered a
value of 2 (with min:max range of 1:10) for parameter ampl.
Define a model to be used for the background and set the parameter values:
sherpa> PARAMPROMPT OFF
Model parameter prompting is off
sherpa> DATA data.dat
sherpa> POISSON[bkgA]
sherpa> BACKGROUND = bkgA
In the third command, the name bkgA is given to
the Sherpa model component POISSON. The final command defines
this model as the model to be used for the background.
Create a background model expression:
sherpa> PARAMPROMPT ON
Model parameter prompting is on
sherpa> BACKGROUND = (POW[modelc])/2
modelc.gamma parameter value [0]
modelc.ref parameter value [1]
modelc.ampl parameter value [1]
This command assigns the model expression
(POW[modelc])/2, to the background model for dataset
number 1. In this example, the user accepted
the given initial values for all of the parameters via parameter
prompting.
See the SOURCE command documentation for more
(analogous) examples.
- sherpa
-
autoest,
create,
create_model,
createparamset,
fit,
freeze,
get_defined_models,
get_model_params,
get_models,
get_num_par,
get_par,
get_stackexpr,
getx,
gety,
guess,
instrument,
integrate,
is_paramset,
jointmode,
kernel,
lineid,
linkparam,
mdl,
modelexpr,
modelstack,
nestedmodel,
noise,
paramprompt,
paramset,
pileup,
rename,
run_fit,
set_par,
set_paramset,
set_stackexpr,
source,
thaw,
truncate,
unlink
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