Basic Lightcurves

CIAO 4.0 Science Threads

Overview

Last Update: 23 Jan 2008 - updated for CIAO 4.0: updated ChIPS syntax; lightcurve tool no longer in CIAO; removed "Tool: dmextract vs. lightcurve" section; filenames, screen output, and region files updated for reprocessed data (version N003 event file for 953)

Synopsis:

The CIAO tool dmextract accurately applies good time interval (GTI) information when creating lightcurves. Its predecessor, lightcurve, was removed from CIAO at version 4.0.

Purpose:

To create lightcurves for use in a variety of analyses.

Related Links:

Why topic: Timing Analysis with Lightcurves: caveats that one should be aware of when doing timing analysis on Chandra data.

Proceed to the HTML or hardcopy (PDF: A4 | letter) version of the thread.

Get Started
ACIS Lightcurves
HRC Lightcurves
Caveats
Parameter files:
- dmextract
- dmextract
History
Images

Get Started

Sample ObsIDs used: 953 (ACIS-I, 47 Tuc); 461 (HRC-I, 3C 273)

File types needed: evt2; dtf1 (ObsID 461 only)

ACIS Lightcurves

The most common lightcurve is made from a point source observed with the ACIS detector. This may be done to get an idea of the variability of the source or to help identify periods of high background.

To begin, we define the regions - two source and one background - which will be used to create the lightcurves. For instructions on how to create regions in ds9, see the Using CIAO Region Files thread. The regions used here are shown in Figure 1 :

unix% more src1.reg
# Region file format: CIAO version 1.0
circle(4010.5,4037.5,8)

unix% more src2.reg
# Region file format: CIAO version 1.0
circle(4035.5,4034.5,8)

unix% more bkg.reg
# Region file format: CIAO version 1.0
circle(3875.5,3972,54.5)

Determine which chips are being used

dmextract uses a ccd_id filter on the input file ensure that the proper GTIs are used. Use dmstat to determine the correct chip:

unix% punlearn dmstat
unix% dmstat "acisf00953N003_evt2.fits[sky=region(src1.reg)][cols ccd_id]"
ccd_id
    min:        3             @:        1 
    max:        3             @:        1 
   mean:        3 
  sigma:        0 
    sum:        7284 
   good:        2428 
   null:        0 

unix% dmstat "acisf00953N003_evt2.fits[sky=region(src2.reg)][cols ccd_id]"
ccd_id
    min:        3             @:        1 
    max:        3             @:        1 
   mean:        3 
  sigma:        0 
    sum:        5850 
   good:        1950 
   null:        0 

unix% dmstat "acisf00953N003_evt2.fits[sky=region(bkg.reg)][cols ccd_id]"
ccd_id
    min:        3             @:        1 
    max:        3             @:        1 
   mean:        3 
  sigma:        0 
    sum:        663 
   good:        221 
   null:        0

The regions with which we are working are all located on chip 3 (ACIS-I3); see Figure 6.1 of the POG for an illustration of the focal plane)

Create a background-subtracted lightcurve

First we extract a background-subtracted lightcurve for "src2":

unix% punlearn dmextract
unix% pset dmextract \
      infile="acisf00953N003_evt2.fits[ccd_id=3,sky=region(src2.reg)][bin time=::2000]"
unix% pset dmextract outfile="src2_sub_lc.fits"
unix% pset dmextract bkg="acisf00953N003_evt2.fits[ccd_id=3,sky=region(bkg.reg)]"
unix% pset dmextract opt="ltc1"
unix% dmextract
Input event file  (acisf00953N003_evt2.fits[ccd_id=3,sky=region(src2.reg)][bin time=::2000]): 
Enter output file name (src2_sub_lc.fits):

You can check the parameter file that was used with plist dmextract.

The lightcurve may be plotted using ChIPS (Note that this example uses the Python interface to ChIPS):

unix% chips

chips> make_figure("src2_sub_lc.fits[cols time,net_rate,err_rate]", "line.color=red")

This lightcurve is shown in Figure 2 . There is a significant drop in count rate near 6.9610x10⁷ seconds. This information is used again in the next section. Quit ChIPS before continuing:

chips> quit

Examining the lightcurve with dmlist shows the same results:

unix% dmlist "src2_sub_lc.fits[cols time,count_rate]" data
 
--------------------------------------------------------------------------------
Data for Table Block LIGHTCURVE
--------------------------------------------------------------------------------
 
ROW    TIME                 COUNT_RATE
 
     1  69584184.1139039993     0.09905969738988
     2  69586184.1139039993           0.08507730
     3  69588184.1139039993         0.0739362250
     4  69590184.1139039993         0.0840644750
     5  69592184.1139039993           0.06482080
     6  69594184.1139039993          0.058743850
     7  69596184.1139039993          0.062795150
     8  69598184.1139039993        0.06532721250
     9  69600184.1139039993        0.07545546250
    10  69602184.1139039993        0.06634003750
    11  69604184.1139039993         0.0840644750
    12  69606184.1139039993         0.0719105750
    13  69608184.1139039993        0.04000658750
    14  69610184.1139039993         0.0010128250
    15  69612184.1139039993          0.050641250
    16  69614184.1139039993           0.04861560
    17  69616184.1139039993     0.04085938928173

Looking for variability

It is also valuable to compare the lightcurves of sources in the same field when looking for variation. Here we extract a lightcurve for each of the "src1" and "src2" regions (intentionally including the background counts):

unix% punlearn dmextract
unix% dmextract outfile="curve_1.fits" opt="ltc1" \
      infile="acisf00953N003_evt2.fits[ccd_id=3,sky=region(src1.reg)][bin time=::2000]"
      
unix% dmextract outfile="curve_2.fits" opt="ltc1" \
      infile="acisf00953N003_evt2.fits[ccd_id=3,sky=region(src2.reg)][bin time=::2000]"

The lightcurves can be plotted in ChIPS:

unix% chips

chips> split(2, 1, 0.01)
chips> make_figure("curve_1.fits[cols time,count_rate]")
chips> current_plot("plot2")
chips> make_figure("curve_2.fits[cols time,count_rate]")

These commands produce Figure 3 .

The results can also be examined with dmlist:

unix% dmlist "curve_1.fits[cols time,count_rate]" data
...
    13  69608184.1139039993        0.07241698750
    14  69610184.1139039993        0.07444263750
    15  69612184.1139039993        0.06937851250
    16  69614184.1139039993        0.08355806250
    17  69616184.1139039993     0.06891210431098

unix% dmlist "curve_2.fits[cols time,count_rate]" data
...
    13  69608184.1139039993        0.04000658750
    14  69610184.1139039993         0.0010128250
    15  69612184.1139039993          0.050641250
    16  69614184.1139039993           0.04861560
    17  69616184.1139039993     0.04085938928173

Comparing this to the lightcurve data from the previous section proves two things:

This is not an intrumental effect, since it shows up in curve_2.fits but not curve_1.fits. An instrumental feature would appear in both sources, as they are close together, on the same chip, etc.
This is not a background feature, since it is present in both the subtracted (src2_sub_lc.fits) and unsubtracted (curve_2.fits) lightcurves.

It is highly likely, therefore, that the dip in count rate is an indication of a variable star. In the case of 47 Tuc, this is due to a binary system; see the Chandra Photo Album entry for 47 Tucanae for more information.

High background levels

The technique for identifying periods of high background from a lightcurve - and subsequently filtering them out - is explained in detail in the Filtering Lightcurves thread.

HRC Lightcurves

The proper method of creating an HRC lightcurve requires accounting for the Dead Time Factor (DTF). The DTF describes the detector's deviation from the standard detection efficiency. This time-dependent change is due to the physical effect of an event striking the micro-channel plate, as illustrated in the MCP Principles page. The DTF is evaluated roughly every 2 seconds and the data are stored in the "dtf1.fits" file. The average DTF value within the time bin is used by dmextract to correct the exposure time and count rate in the lightcurve.

The source region for this example has been saved in the file hrc_src.reg. Again, for instructions on how to create regions in ds9, see the Using CIAO Region Files thread.

unix% more hrc_src.reg 
# Region file format: CIAO version 1.0
circle(16478,16292,19.5)

unix% punlearn dmextract
unix% pset dmextract infile="hrcf00461N003_evt2.fits[sky=region(hrc_src.reg)][bin time=64938947.367:64959159.548:1000]"
unix% pset dmextract outfile=hrc_lc.fits
unix% pset dmextract opt=ltc1
unix% pset dmextract exp=hrcf00461_001N003_dtf1.fits
unix% dmextract 
Input event file  (hrcf00461N003_evt2.fits[sky=region(hrc_src.reg)][bin time=64938947.367:64959159.548:1000]): 
Enter output file name (hrc_lc.fits):

You can check the parameter file that was used with plist dmextract.

Plotting the lightcurve in ChIPS:

unix% chips

chips> make_figure("hrc_lc.fits[cols time,count_rate]")
chips> limits(Y_AXIS, 10.7, AUTO)

creates Figure 4 .

Examine the lightcurve with dmlist:

unix% dmlist "hrc_lc.fits[cols time,count_rate]" data
 
--------------------------------------------------------------------------------
Data for Table Block LIGHTCURVE
--------------------------------------------------------------------------------
 
ROW    TIME                 COUNT_RATE
 
     1  64939447.3669999987        11.0370718045
     2  64940447.3669999987        11.1034571494
     3  64941447.3669999987        11.0080340468
     4  64942447.3669999987        10.7977612541
     5  64943447.3669999987        10.9829147666
     6  64944447.3669999987        10.8403845035
     7  64945447.3669999987        11.1324409057
     8  64946447.3669999987        10.9762097416
     9  64947447.3669999987        11.0741103068
    10  64948447.3669999987        11.0283615145
    11  64949447.3669999987        10.8690184268
    12  64950447.3669999987        10.8724532421
    13  64951447.3669999987        11.0687810980
    14  64952447.3669999987        10.8811205972
    15  64953447.3669999987        10.9841560313
    16  64954447.3669999987        10.9578789351
    17  64955447.3669999987        11.0886790730
    18  64956447.3669999987        10.9611792524
    19  64957447.3669999987        11.1768313479
    20  64958447.3669999987        11.0863053714
    21  64959447.3669999987     0.52092756248201

Refer to the Filtering Lightcurves thread for information on how to handle the low point at ~6.4959E+07 seconds.

Caveats

There are a number of subtleties that it is important to be aware of when using lightcurves for timing analysis. These issues are described in the Timing Analysis with Lightcurves why topic; please read that document before continuing with the analysis.


Parameters for /home/username/cxcds_param/dmextract.par



#--------------------------------------------------------------------
#
# DMEXTRACT -- extract columns or counts from an event list
#
#--------------------------------------------------------------------
        infile = acisf00953N003_evt2.fits[ccd_id=3,sky=region(src2.reg)][bin time=::2000] Input event file 
       outfile = src2_sub_lc.fits Enter output file name
          (bkg = acisf00953N003_evt2.fits[ccd_id=3,sky=region(bkg.reg)]) Background
				  region file or fixed background (counts/pixel/s) subtraction
        (error = gaussian)        Method for error determination(poisson|gaussian|<variance file>)
     (bkgerror = gaussian)        Method for background error determination(poisson|gaussian|<variance file>)
      (bkgnorm = 1.0)             Background normalization
          (exp = )                Exposure map image file
       (bkgexp = )                Background exposure map image file
      (sys_err = 0)               Fixed systematic error value for SYS_ERR keyword
          (opt = ltc1)            Output file type: pha1 
     (defaults = ${ASCDS_CALIB}/cxo.mdb -> /soft/ciao/data/cxo.mdb) Instrument defaults file
         (wmap = )                WMAP filter/binning (e.g. det=8 or default)
      (clobber = no)              OK to overwrite existing output file(s)?
      (verbose = 0)               Verbosity level
         (mode = ql)


Parameters for /home/username/cxcds_param/dmextract.par



#--------------------------------------------------------------------
#
# DMEXTRACT -- extract columns or counts from an event list
#
#--------------------------------------------------------------------
        infile = hrcf00461N003_evt2.fits[sky=region(hrc_src.reg)][bin time=64938947.367:64959159.548:1000] Input event file 
       outfile = hrc_lc.fits      Enter output file name
          (bkg = )                Background region file or fixed background (counts/pixel/s) subtraction
        (error = gaussian)        Method for error determination(poisson|gaussian|<variance file>)
     (bkgerror = gaussian)        Method for background error determination(poisson|gaussian|<variance file>)
      (bkgnorm = 1.0)             Background normalization
          (exp = hrcf00461_001N003_dtf1.fits) Exposure map image file
       (bkgexp = )                Background exposure map image file
      (sys_err = 0)               Fixed systematic error value for SYS_ERR keyword
          (opt = ltc1)            Output file type 
     (defaults = ${ASCDS_CALIB}/cxo.mdb -> /soft/ciao/data/cxo.mdb) Instrument defaults file
         (wmap = )                WMAP filter/binning (e.g. det=8 or default)
      (clobber = no)              OK to overwrite existing output file(s)?
      (verbose = 0)               Verbosity level
         (mode = ql)

History

03 Jan 2005	reviewed for CIAO 3.2: no changes
21 Dec 2005	updated for CIAO 3.3: default value of `dmextract` `error` and `bkgerror` parameters is "gaussian"; `dmextract` can now accept a DTF file in the `exp` parameter, which simplifies the process of creating HRC Lightcurves
01 Dec 2006	updated for CIAO 3.4: CHIPS version
23 Jan 2008	updated for CIAO 4.0: updated ChIPS syntax; `lightcurve` tool no longer in CIAO; removed "Tool: dmextract vs. lightcurve" section; filenames, screen output, and region files updated for reprocessed data (version N003 event file for 953)