EPIC source finding thread: step-by-step

In this thread, we will assume that a user intends to detect sources on a single MOS image, extracted in the 0.2-10 keV band. The event list MOS.evt is assumed to have been already cleaned for high-background intervals, following, e.g., steps 3. to 5. in the edetect_chain thread.

1. set up your SAS environment (following the SAS start-up thread)

2. produce the MOS image in the 0.2-10 keV energy band from the cleaned event list (file MOS.evt

evselect table=MOS.evt:EVENTS imagebinning='binSize' \
imageset='mos_image_full.fits' withimageset=yes xcolumn='X' ycolumn='Y' \
ximagebinsize=80 yimagebinsize=80 \
expression='#XMMEA_EM&&(PI in [200:10000])&&(PATTERN \
in [0:12])&& gti(mos_back_gti.fits,TIME)'

3. create an exposure map

   eexpmap attitudeset=AttHK.ds eventset=MOS.evt imageset=mos_image_full.fits \
   expimageset=MOS_expmap.ds pimin="200" pimax=10000"

   where AttHK.ds is the attitude history file (file *ATT*FIT in the PPS products). An example of exposure map is shown in Fig.1

   
   

   Fig.1: Example of MOS exposure map
    

4. create a detection map (masking the areas of the field of view where source detection shall not not be performed)

   emask expimageset=MOS_expmap.ds threshold1=0.25 detmaskset=MOS_mask.ds

   where threshold1 indicates the maximum exposure fraction for a masked pixel (see the emask parameters description). In this specific example all pixels whose exposure time is lower than 0.25 times the maximum exposure are given value "0" in the mask, and excluded from the area where the source detection is performed. Users may relax this choice after a careful look at the exposure map, if they want to seek sources on a larger area. An example of MOS mask is shown in Fig.2

   
   

   Fig.2: Example of MOS mask
    

5. perform a sliding box detection, using locally estimated background

   eboxdetect usemap=no likemin=8 withdetmask=yes detmasksets=MOS_mask.ds \
   imagesets=mos_image_full.fits expimagesets=MOS_expmap.ds pimin=200 \
   pimax=10000 boxlistset=eboxlist_local.fits

   where likemin is the minimum detection likelihood (see the eboxdetect parameters description as well).

6. creating a background map, after masking the sources detected during the previous step:

   esplinemap bkgimageset=MOS_bkg.ds scut=0.005 imageset=mos_image_full.fits \
   nsplinenodes=16 withdetmask=yes detmaskset=MOS_mask.ds withexpimage=yes \
   expimageset=MOS_expmap.ds

   where nsplinenodes is the number of nodes employed in the background map interpolation and scut the source surface brightness level (in counts per arcseconds squared), above which a source is masked (see the esplinemap parameters description). Particular care needs to be used in the choice of nsplinenodes. A small number of nodes may fail to accurately reproduce the background spatial distribution; an excessive number of nodes may create faked structures. The optimal compromise depends on the spatial pattern of the X-ray emission (background+sources) in a given field of view. Users are encouraged to try different choices for this parameter, and compare the resulting maps with the observed background spatial distribution pattern. The parameter scut mainly drives the amount of source contamination to the interpolated background map, due to the broad wings of the Point Spread Function. The best value of scut is determined by the optimal balance between background integration area and acceptable source contamination level. This is obviously dependent on the amount of background and the on crowdedness of the field. Users are encouraged to check the background maps produced by esplinemap for short-scale fluctuations, and tune the scut value accordingly.
   An example of MOS background map is shown in Fig.3.

   
   

   Fig.3: Example of MOS background map
    

7. perform a second run of source detection, using the background map calculated during the previous step

   eboxdetect usemap=yes bkgimagesets=MOS_bkg.ds likemin=8 withdetmask=yes \
   detmaskset=MOS_mask.ds imagesets=mos_image_full.fits \
   expimagesets=MOS_expmap.ds pimin=200 pimax=10000 boxlistset=eboxlist_map.fits

8. maximum likelihood fitting on the sources detected in the previous step, to:
   • optimize source centroiding (constrained to be the same for all the cameras and/or energy bands, if source detection is performed on multiple images)
   • determine source extend by fitting the local Point Spread Function

   emldetect imagesets=mos_image_full.fits expimagesets=MOS_expmap.ds \
   bkgimagesets=MOS_bkg.ds boxlistset=eboxlist_map.fits ecf=2.0 \
   mllistset=emllist.fits mlmin=10 determineerrors=yes

9. create sensitivity maps

   esensmap expimagesets=MOS_expmap.ds bkgimagesets=MOS_bkg.ds \
   detmasksets=MOS_mask.ds mlmin=10 sensimageset=MOS_sens_map.fits

   i.e. a pixel-by-pixel detection upper limit map. An example of MOS sensitivity map is shown in Fig.4.

   
   

   Fig.4: Example of MOS sensitivity map
    

10. Display the location of the detected sources on the MOS image

srcdisplay boxlistset=emllist.fits imageset=mos_image_full.fits sourceradius=0.01