Jump to: Description · Examples · Parameters · What Can You Do With it? · Changes in CIAO 4.5 · Bugs · See Also

AHELP for CIAO 4.5

mean_energy_map

Context: tools

Synopsis

Creates an image whose pixels are the mean energy of the events

Syntax

mean_energy_map  infile outfile binsize [pbkfile] [tmpdir] [verbose]
[clobber]

`mean_energy_map' takes an input ACIS event file and computes an output image whose pixels are the exposure-corrected mean energy of the events. This is not a temperature map, which traditionally would be constructed by fitting a thermal model to the event pulse height distribution. However, some authors (David et al. 2009, ApJ, 705, 624) have shown how to convert this kind of mean energy map to a temperature map.

mean_energy_map takes the input event file and runs the eff2evt tool to compute the flux for each event where FLUX = ENERGY * Efficiency. dmtcalc is used to separate out the the efficiency (i.e. weight) term. Finally, dmimgcalc is used to create two images: sum(flux) and sum(weight); dividing the two gives the exposure-weighted mean energy of the events in each pixel.

Example 1

mean_energy_map infile=evt.fits outfile=me_map.fits bin=8

Create the mean energy map from the input event file. Since no energy filter has been applied, all energies will be used. This may signficantly bias the results since high-energy background events will dominate the output.

Example 2

mean_energy_map infile="evt.fits[energy=500:1200]" outfile=me_map.fits
bin=8

Repeating the previous example, but applying an energy filter so that only events in the 500 to 1200 eV range are used. Without any spatial filtering, the entire 8k x 8k ACIS image space is used.

Example 3

mean_energy_map infile="evt.fits[energy=500:1200,sky=region(ds9.reg)]"
outfile=me_map.fits bin=8

A region filter is applied to the input file so that the output image will be just large enough to enclose the specified region.

Parameters

name	type	ftype	def	min	max	reqd
infile	file	input				yes
outfile	file	output				yes
binsize	float					yes
pbkfile	file	input				no
tmpdir	file	output				no
verbose	integer		0	0	5
clobber	boolean		no

Detailed Parameter Descriptions

Parameter=infile `(file required filetype=input)`

Input ACIS event file

The event file should include an energy filter to remove any high-energy background events. Otherwise, the results may signficantly biased by the background energies.

HRC data does not contain energy information and therefore cannot be used as input to mean_energy_map.

Parameter=outfile `(file required filetype=output)`

The output image

The output image is in units of eV and represents the exposure-corrected mean energy of the events in each pixel.

Parameter=binsize `(float required)`

Output image blocking factor

The blocking factor should be chosen such that you get a suitable number of events in each pixel of interest to make the mean be meaningful. Computing the mean of a single event is very noisy. Picking this number will depend on the surface brightness of the source, length of observation, energy distribution, etc.

Parameter=pbkfile `(file not required filetype=input)`

Name of the parameter block file

Parameter=tmpdir `(file not required filetype=output)`

Directory name for temporary files

Parameter=verbose `(integer default=0 min=0 max=5)`

The tool chatter level

Parameter=clobber `(boolean default=no)`

Clobber outfile

Remove outfile if it already exists?

What Can You Do With it?

The output mean energy map may look noisy, especially if a small binsize was used. You can smooth the map with the aconvolve tool. (The csmooth tool cannot be used, since it requires the pixel values to be integer counts.)

    unix% aconvolve me_map.fits sm_me_map.fits "lib:tophat(2,1,3,3)"

It is also possible to adaptively smooth the mean energy map by first smoothing the counts image, and then using the scales/radii to smooth the mean energy map.

    unix% dmimgadapt evt.fits"[energy=200:3000][bin sky=8]" \
    outfile=smimg.fits counts=100 radfile=radii.fits \
    function=tophat min=0.5 max=30 num=60 radscal=linear clob+

    unix% dmimgadapt me_map.fits adaptsm_me_map.fits \
    inrad=radii.fits function=tophat mode=h clob+

Apply an adaptive binning scheme to the mean energy map to get mean values with an approximately fixed number of events:

    unix% dmnautilus evt.fits"[energy=200:3000][bin sky=8]" \
    outfile=abin.fits snr=10 outmask=msk.fits

    unix% dmmaskbin infile=me_map.fits mask=msk.fits out=abin_me_map.fits

If a conversion from energy to temperature can be written in a simple functional form, then dmimgcalc can be used to perform the conversion.

The example conversion is specific to the data set presented in David et al.

    unix% dmimgcalc sm_me_map.fits none temperature_map.fits \
    op="imgout=(((img1/1000.0)*5.88)-4.61)"

Changes in CIAO 4.5

mean_energy_map creates image whose pixels are the exposure-weighted mean energy of the pixel.

Bugs

There are no known bugs for this tool.

mean_energy_map

Parameter=infile (file required filetype=input)

Parameter=outfile (file required filetype=output)

Parameter=binsize (float required)

Parameter=pbkfile (file not required filetype=input)

Parameter=tmpdir (file not required filetype=output)

Parameter=verbose (integer default=0 min=0 max=5)