Äîêóìåíò âçÿò èç êýøà ïîèñêîâîé ìàøèíû. Àäðåñ îðèãèíàëüíîãî äîêóìåíòà : http://xmm.vilspa.esa.es/sas/7.0.0/documentation/threads/src_find_thread.html
Äàòà èçìåíåíèÿ: Tue Aug 22 17:34:21 2006
Äàòà èíäåêñèðîâàíèÿ: Sat Dec 22 14:47:09 2007
Êîäèðîâêà:
Ïîèñêîâûå ñëîâà: èçó÷åíèå ëóíû

EPIC source finding thread: <tt>edetect_chain</tt>

EPIC source finding thread: `edetect_chain`

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), 0.5-2 keV, 2-4.5 keV, 4.5-7.5 keV, 7.5-12 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, they bear the names: MOS1.evt, MOS2.evt, and PN.evt, respectively.

set up your SAS environment (following the SAS start-up thread)
point to the directory containing the calibrated event lists (eg. from the pipeline)

setenv DATRED [path_to_my_reduced_data_directory]

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

plot the lightcurves 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

establish Good Time Intervals (GTIs) for every camera, since exposure coverage can be different (cut here: 1 count/s)

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

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 [500:2000])&&(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 [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_b4.fits' \
withimageset=yes xcolumn='X' ycolumn='Y' ximagebinsize=40 yimagebinsize=40 \
expression='#XMMEA_EM&&(PI in [4500:7500])&&(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 [7500:12000])&&(PATTERN in [0:12])&&(FLAG==0) && gti(m1_back_gti.fits,TIME)'

Check the quality of the images, eg.

ds9 m1_image_b3.fits

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 [500:2000])&&(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 [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_b4.fits' \
withimageset=yes xcolumn='X' ycolumn='Y' ximagebinsize=40 yimagebinsize=40 \
expression='#XMMEA_EM&&(PI in [4500:7500])&&(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 [7500:12000])&&(PATTERN in [0:12])&&(FLAG==0) && gti(m2_back_gti.fits,TIME)'

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 [500:2000])&&(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 [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_b4.fits' \
withimageset=yes xcolumn='X' ycolumn='Y' ximagebinsize=80 yimagebinsize=80 \
expression='#XMMEA_EP&&(PI in [4500:7500])&&(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 [7500:12000])&&(PATTERN in [0:4])&&(FLAG==0) && gti(pn_back_gti.fits,TIME)'

Run detection chains for all 3 EPICs (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 2000 4500 7500' pimax='500 2000 4500 7500 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 2000 4500 7500' pimax='500 2000 4500 7500 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 2000 4500 7500' pimax='500 2000 4500 7500 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 the 1XMM EPIC Source Catalogue User Guide).

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

EPIC source finding thread: edetect_chain

EPIC source finding thread: `edetect_chain`