Include a model of the Chandra ACIS pile up when fitting PHA data.
Chandra observations of bright sources can be affected by pileup, so that there is a non-linear correlation between the source model and the predicted counts. This process can be modelled by including the jdpileup model for a data set, using the set_pileup_model.
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 (an instance of the sherpa.astro.models.JDPileup class) –
Delete the pile up model for a data set.
Fit one or more data sets.
Return the pile up model for a data set.
The ACIS pile up model.
List of all the data sets with a pile up model.
Define the convolved model expression for a data set.
Set the source 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 model parameter. If given two un-named arguments, then they are interpreted as the id and model parameters, respectively.
This is a generic function, and can be used to model other non-linear detector effects, but at present the only available model is for the ACIS pile up provided by the jdpileup model.
Plot up the model (an xsphabs model multiplied by a powlaw1d component) and then overplot the same expression but including the effects of pile up in the Chandra ACIS instrument:
>>> load_pha('src.pi') >>> set_source(xsphabs.gal * powlaw1d.pl) >>> plot_model() >>> set_pileup_model(jdpileup.jpd) >>> plot_model(overplot=True)