How to evaluate the pile-up fraction
This thread illustrates you how to evaluate the pile-up fraction.
Pile-up occurs whenever more than one photon is read in a pixel during
a read-out cycle. These photons are interpreted as a single one, whose
energy is equal to the sum of the individual energies of the incoming
photons. The effect of pile-up on the spectra is therefore three-fold:
- photon loss, either due to the fact that one photon is "read"
instead of several, or to the fact that the summed energy may assume
values beyond the upper energy on-board threshold
- energy distortion, whereby photons are moved
to harder X-ray regions of the spectrum
- pattern migration, whereby photon-induced change clouds have
a higher likelihood to merge, and the expected
pattern distribution is therefore distorted
To evaluate whether pile-up may be a problem for a source in your
EPIC field-of-view, you
should use the SAS task
epatplot, following
these steps:
- identify the centroid position and extraction radius of your source,
using the procedure explained in the
MOS or
pn spectrum extraction thread
(point 6 to 10)
- extract a filtered event list, including only photons within the
above region. If the region has a centroid position (25600, 23900)
and radius (640) in sky coordinates, and the original event
list file name is mos1.evt
evselect table=mos1.evt withfilteredset=yes filteredset=mos1_filtered.evt \
keepfilteroutput=yes expression="((X,Y) in circle(25600,23900,640))"
- apply the task
epatplot on the filtered
event list, and produce a POSTSCRIPT file, displaying the result plots
epatplot set=mos1_filtered.evt device="/CPS" plotfile="mos1_filtered_pat.ps"
The last command produces a POSTSCRIPT file mos1_filtered_pat.ps like
the following:
Fig.1: epatplot output
file
This plot contains two panels:
- the upper panel shows a spectrum (distribution of counts
as a function of the PI channel: be aware that the quantity on the X-axis
is not is physical units) for each event pattern class
- the lower panel shows the expected pattern distribution functions
(smooth solid lines) superposed to the observed ones
(histogram)
In all plots, the distributions
corresponding to different pattern classes
are recognizable through colors: red
for single, blue for double, green for triple, and
turquoise for quadruple events.
Your source spectrum will be affected by pile-up if - as in the case shown
in Fig.1 - the expected distributions are significantly discrepant from
the observed one.
For instructions on how to deal with piled-up spectra, the user is referred
to the
XMM-Newton Users' Handbook or to the
SAS pile-up watchout
item.