EPIC source finding thread: edetect_chain

Introduction This tread shows how to produce optimally background-filtered images in five energy bands (from a MOS camera event list in the example), and apply on them the source detection meta-task edetect_chain. Expected Outcome Besides the optimally background-filtered images and the associated background light curves, users will be able to generate a single list of the detected sources, as well as a number of associated products such as background and sensitivity maps. SAS Tasks to be Used dsplot edetect_chain evselect tabgtigen Prerequisites SAS Start-up Thread How to generate EPIC calibrated and concatenated event lists Useful Links This thread makes use of the image display and analysis package ds9. Caveats

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Procedure

This thread contains a step-by-step recipe to perform a simultaneous EPIC sources searching on 5 images extracted in the 0.2-0.5 keV (0.3-0.5 keV by PN), 0.5-1 keV, 1-2 keV, 2-4.5 keV, 4.5-12 keV energy bands, respectively. This thread illustrated the usage of the SAS source detection script edetect_chain, which automatically runs all the steps of the detection source algorithm with one single command. Users interested to perform individually each step of the same algorithm may refer to the EPIC source detection step-by-step thread. In this thread, the event files of the EPIC cameras bear the names: MOS1.evt, MOS2.evt, and PN.evt, respectively.

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


2. point to the directory containing the calibrated event lists (e.g. from emproc/ epproc or emchain/ epchain. )
   
   setenv DATRED [path_to_my_reduced_data_directory]
   
   
3. extract single event (i.e. pattern zero only), high energy (E > 10 keV) light curves, to identify intervals of flaring particle background
   
   evselect table=$DATRED/MOS1.evt:EVENTS expression='#XMMEA_EM&&(PI>10000)&&(PATTERN==0)' \
     rateset="m1_back_lightc.fits" \
     timebinsize=10 withrateset=yes maketimecolumn=yes makeratecolumn=yes
   
   evselect table=$DATRED/MOS2.evt:EVENTS expression='#XMMEA_EM&&(PI>10000)&&(PATTERN==0)' \
     rateset="m2_back_lightc.fits" \
     timebinsize=10 withrateset=yes maketimecolumn=yes makeratecolumn=yes
   
   evselect table=$DATRED/PN.evt:EVENTS expression='#XMMEA_EP&&(PI>10000)&&(PATTERN==0)' \
     rateset="pn_back_lightc.fits" \
     timebinsize=10 withrateset=yes maketimecolumn=yes makeratecolumn=yes
   
   
4. plot the light curves to decide about the cut to be applied for rejection of flaring periods
   
   dsplot table=m1_back_lightc.fits x=TIME y=RATE
   dsplot table=m2_back_lightc.fits x=TIME y=RATE
   dsplot table=pn_back_lightc.fits x=TIME y=RATE
   
   
   
   
5. establish Good Time Intervals (GTIs) for every camera, since exposure coverage can be different
   
   tabgtigen table=m1_back_lightc.fits expression="RATE<0.35" gtiset=m1_back_gti.fits
   tabgtigen table=m2_back_lightc.fits expression="RATE<0.35" gtiset=m2_back_gti.fits
   tabgtigen table=pn_back_lightc.fits expression="RATE<1.0" gtiset=pn_back_gti.fits
   
   
6. Produce images for MOS1 in 5 energy bands and from the whole spectral coverage
   
   evselect table=$DATRED/MOS1.evt:EVENTS imagebinning='binSize' imageset='m1_image_full.fits' \
     withimageset=yes xcolumn='X' ycolumn='Y' ximagebinsize=40 yimagebinsize=40 \
     expression='#XMMEA_EM&&(PI in [200:12000])&&(PATTERN in [0:12])&&(FLAG==0) && gti(m1_back_gti.fits,TIME)'
   
   evselect table=$DATRED/MOS1.evt:EVENTS  imagebinning='binSize' imageset='m1_image_b1.fits' \
     withimageset=yes xcolumn='X' ycolumn='Y' ximagebinsize=40 yimagebinsize=40 \
     expression='#XMMEA_EM&&(PI in [200:500])&&(PATTERN in [0:12])&&(FLAG==0) && gti(m1_back_gti.fits,TIME)'
   
   evselect table=$DATRED/MOS1.evt:EVENTS  imagebinning='binSize' imageset='m1_image_b2.fits' \
     withimageset=yes xcolumn='X' ycolumn='Y' ximagebinsize=40 yimagebinsize=40 \
     expression='#XMMEA_EM&&(PI in [500:1000])&&(PATTERN in [0:12])&&(FLAG==0) && gti(m1_back_gti.fits,TIME)'
   
   evselect table=$DATRED/MOS1.evt:EVENTS  imagebinning='binSize' imageset='m1_image_b3.fits' \
     withimageset=yes xcolumn='X' ycolumn='Y' ximagebinsize=40 yimagebinsize=40 \
     expression='#XMMEA_EM&&(PI in [1000:2000])&&(PATTERN in [0:12])&&(FLAG==0) && gti(m1_back_gti.fits,TIME)'
   
   evselect table=$DATRED/MOS1.evt:EVENTS  imagebinning='binSize' imageset='m1_image_b4.fits' \
     withimageset=yes xcolumn='X' ycolumn='Y' ximagebinsize=40 yimagebinsize=40 \
     expression='#XMMEA_EM&&(PI in [2000:4500])&&(PATTERN in [0:12])&&(FLAG==0) && gti(m1_back_gti.fits,TIME)'
   
   evselect table=$DATRED/MOS1.evt:EVENTS  imagebinning='binSize' imageset='m1_image_b5.fits' \
     withimageset=yes xcolumn='X' ycolumn='Y' ximagebinsize=40 yimagebinsize=40 \
     expression='#XMMEA_EM&&(PI in [4500:12000])&&(PATTERN in [0:12])&&(FLAG==0) && gti(m1_back_gti.fits,TIME)'
   
   
7. Check the quality of the images, e.g.
   
   ds9 m1_image_b3.fits
   
   
   
   
   
8. Produce images for MOS2 in 5 energy bands and from the whole spectral coverage
   
   evselect table=$DATRED/MOS2.evt:EVENTS imagebinning='binSize' imageset='m2_image_full.fits' \
     withimageset=yes xcolumn='X' ycolumn='Y' ximagebinsize=40 yimagebinsize=40 \
     expression='#XMMEA_EM&&(PI in [200:12000])&&(PATTERN in [0:12])&&(FLAG==0) && gti(m2_back_gti.fits,TIME)'
   
   evselect table=$DATRED/MOS2.evt:EVENTS  imagebinning='binSize' imageset='m2_image_b1.fits' \
     withimageset=yes xcolumn='X' ycolumn='Y' ximagebinsize=40 yimagebinsize=40 \
     expression='#XMMEA_EM&&(PI in [200:500])&&(PATTERN in [0:12])&&(FLAG==0) && gti(m2_back_gti.fits,TIME)'
   
   evselect table=$DATRED/MOS2.evt:EVENTS  imagebinning='binSize' imageset='m2_image_b2.fits' \
     withimageset=yes xcolumn='X' ycolumn='Y' ximagebinsize=40 yimagebinsize=40 \
     expression='#XMMEA_EM&&(PI in [500:1000])&&(PATTERN in [0:12])&&(FLAG==0) && gti(m2_back_gti.fits,TIME)'
   
   evselect table=$DATRED/MOS2.evt:EVENTS  imagebinning='binSize' imageset='m2_image_b3.fits' \
     withimageset=yes xcolumn='X' ycolumn='Y' ximagebinsize=40 yimagebinsize=40 \
     expression='#XMMEA_EM&&(PI in [1000:2000])&&(PATTERN in [0:12])&&(FLAG==0) && gti(m2_back_gti.fits,TIME)'
   
   evselect table=$DATRED/MOS2.evt:EVENTS  imagebinning='binSize' imageset='m2_image_b4.fits' \
     withimageset=yes xcolumn='X' ycolumn='Y' ximagebinsize=40 yimagebinsize=40 \
     expression='#XMMEA_EM&&(PI in [2000:4500])&&(PATTERN in [0:12])&&(FLAG==0) && gti(m2_back_gti.fits,TIME)'
   
   evselect table=$DATRED/MOS2.evt:EVENTS  imagebinning='binSize' imageset='m2_image_b5.fits' \
     withimageset=yes xcolumn='X' ycolumn='Y' ximagebinsize=40 yimagebinsize=40 \
     expression='#XMMEA_EM&&(PI in [4500:12000])&&(PATTERN in [0:12])&&(FLAG==0) && gti(m2_back_gti.fits,TIME)'
   
   
9. Produce images for PN
   
   evselect table=$DATRED/PN.evt:EVENTS imagebinning='binSize' imageset='pn_image_full.fits' \
     withimageset=yes xcolumn='X' ycolumn='Y' ximagebinsize=80 yimagebinsize=80 \
     expression='#XMMEA_EP&&(PI in [300:12000])&&(PATTERN in [0:4])&&(FLAG==0) && gti(pn_back_gti.fits,TIME)'
   
   evselect table=$DATRED/PN.evt:EVENTS  imagebinning='binSize' imageset='pn_image_b1.fits' \
     withimageset=yes xcolumn='X' ycolumn='Y' ximagebinsize=80 yimagebinsize=80 \
     expression='#XMMEA_EP&&(PI in [300:500])&&(PATTERN in [0:4])&&(FLAG==0) && gti(pn_back_gti.fits,TIME)'
   
   evselect table=$DATRED/PN.evt:EVENTS  imagebinning='binSize' imageset='pn_image_b2.fits' \
     withimageset=yes xcolumn='X' ycolumn='Y' ximagebinsize=80 yimagebinsize=80 \
     expression='#XMMEA_EP&&(PI in [500:1000])&&(PATTERN in [0:4])&&(FLAG==0) && gti(pn_back_gti.fits,TIME)'
   
   evselect table=$DATRED/PN.evt:EVENTS  imagebinning='binSize' imageset='pn_image_b3.fits' \
     withimageset=yes xcolumn='X' ycolumn='Y' ximagebinsize=80 yimagebinsize=80 \
     expression='#XMMEA_EP&&(PI in [1000:2000])&&(PATTERN in [0:4])&&(FLAG==0) && gti(pn_back_gti.fits,TIME)'
   
   evselect table=$DATRED/PN.evt:EVENTS  imagebinning='binSize' imageset='pn_image_b4.fits' \
     withimageset=yes xcolumn='X' ycolumn='Y' ximagebinsize=80 yimagebinsize=80 \
     expression='#XMMEA_EP&&(PI in [2000:4500])&&(PATTERN in [0:4])&&(FLAG==0) && gti(pn_back_gti.fits,TIME)'
   
   evselect table=$DATRED/PN.evt:EVENTS  imagebinning='binSize' imageset='pn_image_b5.fits' \
     withimageset=yes xcolumn='X' ycolumn='Y' ximagebinsize=80 yimagebinsize=80 \
     expression='#XMMEA_EP&&(PI in [4500:12000])&&(PATTERN in [0:4])&&(FLAG==0) && gti(pn_back_gti.fits,TIME)'
   
   
10. Run detection chains for all 3 EPIC (they could be also combined in one call) - Note the different conversion factors for MOS1 (Thin filter) and MOS2 (Thick filter)
    
    edetect_chain imagesets='"m1_image_b1.fits" "m1_image_b2.fits" "m1_image_b3.fits" "m1_image_b4.fits" "m1_image_b5.fits"' \
      eventsets=$DATRED/MOS1.evt attitudeset=$DATRED/AttHk.ds \
      pimin='200 500 1000 2000 4500' pimax='500 1000 2000 4500 12000' \
      ecf='1.772 1.977 0.745 0.277 0.030' \
      eboxl_list='m1_eboxlist_l.fits' eboxm_list='m1_eboxlist_m.fits' \
      esp_nsplinenodes=16 eml_list='m1_emllist.fits' esen_mlmin=15
    
    edetect_chain imagesets='"m2_image_b1.fits" "m2_image_b2.fits" "m2_image_b3.fits" "m2_image_b4.fits" "m2_image_b5.fits"' \
      eventsets=$DATRED/MOS2.evt attitudeset=$DATRED/AttHk.ds \
      pimin='200 500 1000 2000 4500' pimax='500 1000 2000 4500 12000' \
      ecf='0.994 1.620 0.706 0.273 0.030' \
      eboxl_list='m2_eboxlist_l.fits' eboxm_list='m2_eboxlist_m.fits' \
      esp_nsplinenodes=16 eml_list='m2_emllist.fits' esen_mlmin=15
    
    edetect_chain imagesets='"pn_image_b1.fits" "pn_image_b2.fits" "pn_image_b3.fits" "pn_image_b4.fits" "pn_image_b5.fits"' \
      eventsets=$DATRED/PN.evt attitudeset=$DATRED/AttHk.ds \
      pimin='300 500 1000 2000 4500' pimax='500 1000 2000 4500 12000' \
      ecf='8.970 6.596 1.953 0.941 0.240' \
      eboxl_list='pn_eboxlist_l.fits' eboxm_list='pn_eboxlist_m.fits' \
      esp_nsplinenodes=16 eml_list='pn_emllist.fits' esen_mlmin=15
    
    The file AttHk.ds is the attitude file generated by the SAS task attcalc (silently run by the EPIC reduction tasks epproc, and emproc). The ecf are the Energy Correction Factors to convert count rates (counts/s) to fluxes (10^-11 erg/s/cm²) in a given energy band (a standard definition is reported in Section 6.2.1 of the 2XMM EPIC Source Catalogue User Guide).
    
    
11. Display the detected source on top the full energy bandpass image
    
    srcdisplay boxlistset=m1_emllist.fits imageset=m1_image_full.fits sourceradius=0.01
    srcdisplay boxlistset=m2_emllist.fits imageset=m2_image_full.fits sourceradius=0.01
    srcdisplay boxlistset=pn_emllist.fits imageset=pn_image_full.fits sourceradius=0.01
    
    
    
    

Last Updated: 8 June 2010

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Caveats None

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