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Basic Lightcurves

CIAO 4.17 Science Threads

Overview

Synopsis:

A simple lightcurve from a point source in ACIS data can be used to get an idea of the variability of the source or to look for background flares that should be filtered out. The process for HRC is similar, but requires accounting for the Dead Time Factor (DTF). The CIAO tool dmextract is used in this thread as it accurately applies good time interval (GTI) information when creating lightcurves.

Purpose:

To create lightcurves for use in a variety of analyses.

Related Links:

Last Update: 25 Jan 2022 - Reviewed for CIAO 4.14. Updated for Repro-5/CALDB 4.9.6

Contents

Get Started

Download the sample data: 953 (ACIS-I, 47 Tuc); 461 (HRC-I, 3C 273)

unix% download_chandra_obsid 953,461 evt2,dtf

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 in this example are shown in Figure 1.

Figure 1: Image of 47 Tuc with extraction regions

[Thumbnail image: Two source extraction regions (src1 and src2) and a background extraction region (bkg) are defined on the data.]

[Version: full-size]

[Print media version: Two source extraction regions (src1 and src2) and a background extraction region (bkg) are defined on the data.]

Figure 1: Image of 47 Tuc with extraction regions

The source and background regions for creating the lightcurves. 

unix% cat src1.reg
# Region file format: CIAO version 1.0
circle(4011.0,4026.1,8)

unix% cat src2.reg
# Region file format: CIAO version 1.0
circle(4034.9,4023.6,8)

unix% cat 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 "acisf00953N005_evt2.fits[sky=region(src1.reg)][cols ccd_id]"
ccd_id
    min:	3 	      @:	1 
    max:	3 	      @:	1 
   mean:	3 
  sigma:	0 
    sum:	7296 
   good:	2432 
   null:	0 
   
unix% dmstat "acisf00953N005_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 "acisf00953N005_evt2.fits[sky=region(bkg.reg)][cols ccd_id]"
ccd_id
    min:	3 	      @:	1 
    max:	3 	      @:	1 
   mean:	3 
  sigma:	0 
    sum:	687 
   good:	229 
   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="acisf00953N005_evt2.fits[ccd_id=3,sky=region(src2.reg)][bin time=::2000]"
unix% pset dmextract outfile="src2_sub_lc.fits"
unix% pset dmextract bkg="acisf00953N005_evt2.fits[ccd_id=3,sky=region(bkg.reg)]"
unix% pset dmextract opt="ltc1"
unix% dmextract
Input event file  (acisf00953N005_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 contents of the dmextract output can be shown with dmlist 

unix% dmlist src2_sub_lc.fits cols

--------------------------------------------------------------------------------
Columns for Table Block LIGHTCURVE
--------------------------------------------------------------------------------
 
ColNo  Name                 Unit        Type             Range
   1   TIME_BIN             channel      Int4           1:17                 S/C TT corresponding to mid-exposure
   2   TIME_MIN             s            Real8          69583184.1139039993: 69616983.4901389927 Minimum Value in Bin
   3   TIME                 s            Real8          69583184.1139039993: 69616983.4901389927 S/C TT corresponding to mid-exposure
   4   TIME_MAX             s            Real8          69583184.1139039993: 69616983.4901389927 Maximum Value in Bin
   5   COUNTS               count        Int4           -                    Counts
   6   STAT_ERR             count        Real8          0:+Inf               Statistical error
   7   AREA                 pixel**2     Real8          -Inf:+Inf            Area of extraction
   8   EXPOSURE             s            Real8          -Inf:+Inf            Time per interval
   9   COUNT_RATE           count/s      Real8          0:+Inf               Rate
  10   COUNT_RATE_ERR       count/s      Real8          0:+Inf               Rate Error
  11   BG_COUNTS            count        Real8          -Inf:+Inf            Background Counts
  12   BG_ERR               count        Real8          -Inf:+Inf            Error on Background counts
  13   BG_AREA              pixel**2     Real8          -Inf:+Inf            Background Area of Extraction
  14   BG_EXPOSURE          s            Real8          -Inf:+Inf            Exposure time of background file
  15   BG_RATE              count/s      Real8          -Inf:+Inf            Background Rate
  16   NORM_BG_COUNTS       count        Real8          -Inf:+Inf            Background Counts
  17   NORM_BG_ERR          count        Real8          -Inf:+Inf            Error on Background counts
  18   NET_COUNTS           count        Real8          -Inf:+Inf            Net Counts
  19   NET_ERR              count        Real8          -Inf:+Inf            Error on Net Counts
  20   NET_RATE             count/s      Real8          -Inf:+Inf            Net Count Rate
  21   ERR_RATE             count/s      Real8          -Inf:+Inf            Error Rate
 
--------------------------------------------------------------------------------
World Coord Transforms for Columns in Table Block LIGHTCURVE
--------------------------------------------------------------------------------
 
ColNo    Name
2:    DT_MIN               = +0 [s] +1.0 * (TIME_MIN  -69584184.113904)
3:    DT                   = +0 [s] +1.0 * (TIME  -69584184.113904)
4:    DT_MAX               = +0 [s] +1.0 * (TIME_MAX  -69584184.113904)

The output file contains various columns including the time bin boundaries, source and background counts, area, as well as exposure times and net count rates. In addition, the TIME column has a coordinate system attached to it that provides the time relative to the start of the observation, DT.

The lightcurve may be plotted using matplotlib 

unix% python

>>> from pycrates import read_file
>>> import matplotlib.pylab as plt
>>> 
>>> tab = read_file("src2_sub_lc.fits")
>>> dt = tab.get_column("dt").values
>>> rate = tab.get_column("net_rate").values
>>> erate = tab.get_column("err_rate").values
>>> 
>>> plt.errorbar(dt, rate, yerr=erate, marker="o", color="red", mfc="black",mec="black", ecolor="grey")
>>> plt.xlabel("$\Delta$ T (sec)")
>>> plt.ylabel("Net Count Rate (counts/sec)")
>>> plt.title("src2_sub_lc.fits")
>>> plt.show()

This lightcurve is shown in Figure 2. There is a significant drop in count rate near 26000 seconds into the observation. This information is used again in the next section.

Figure 2: Background-subtracted lightcurve of src2

[Thumbnail image: The points of the lightcurve are plotted with a solid red line connecting white cross symbols and up and down errors]

[Version: full-size]

[Print media version: The points of the lightcurve are plotted with a solid red line connecting white cross symbols and up and down errors]

Figure 2: Background-subtracted lightcurve of src2

There is a significant drop in count rate near 26000 seconds into the observations.

Examining the lightcurve with dmlist shows the same results: 

unix% % dmlist "src2_sub_lc.fits[cols dt,time,count_rate]" data
 
--------------------------------------------------------------------------------
Data for Table Block LIGHTCURVE
--------------------------------------------------------------------------------
 
ROW    DT                   TIME                 COUNT_RATE
 
     1                    0  69584184.1139039993     0.10108994807374
     2               2000.0  69586184.1139039993           0.08507730
     3               4000.0  69588184.1139039993         0.0739362250
     4               6000.0  69590184.1139039993         0.0840644750
     5               8000.0  69592184.1139039993        0.06431438750
     6              10000.0  69594184.1139039993          0.058743850
     7              12000.0  69596184.1139039993          0.062795150
     8              14000.0  69598184.1139039993        0.06532721250
     9              16000.0  69600184.1139039993          0.074949050
    10              18000.0  69602184.1139039993        0.06634003750
    11              20000.0  69604184.1139039993         0.0840644750
    12              22000.0  69606184.1139039993         0.0719105750
    13              24000.0  69608184.1139039993        0.04000658750
    14              26000.0  69610184.1139039993         0.0010128250
    15              28000.0  69612184.1139039993          0.050641250
    16              30000.0  69614184.1139039993           0.04861560
    17              32000.0  69616184.1139039993     0.04089879081378

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="acisf00953N005_evt2.fits[ccd_id=3,sky=region(src1.reg)][bin time=::2000]"
      
unix% dmextract outfile="curve_2.fits" opt="ltc1" \
      infile="acisf00953N005_evt2.fits[ccd_id=3,sky=region(src2.reg)][bin time=::2000]"

The lightcurves can be plotted together: 

unix% python

>>> from pycrates import read_file
>>> import matplotlib.pylab as plt
>>> 
>>> plt.subplots(2,1,sharex="col")
>>> plt.subplots_adjust(hspace=0.4)
>>> plt.subplot(2,1,2)
>>> 
>>> tab = read_file("curve_1.fits")
>>> dt = tab.get_column("dt")
>>> rate = tab.get_column("count_rate")
>>> erate = tab.get_column("count_rate_err")
>>> plt.errorbar(dt.values, rate.values, yerr=erate.values, 
    marker="o", color="red", mfc="black",mec="black", ecolor="grey")
>>> plt.xlabel(r"$\Delta$ T (s)")
>>> plt.ylabel("Count Rate (count/s)")
>>> 
>>> plt.subplot(2,1,1)
>>> tab = read_file("curve_2.fits")
>>> dt = tab.get_column("dt")
>>> rate = tab.get_column("count_rate")
>>> erate = tab.get_column("count_rate_err")
>>> plt.errorbar(dt.values, rate.values, yerr=erate.values, 
    marker="o", color="red", mfc="black",mec="black", ecolor="grey")
>>> plt.xlabel(r"$\Delta$ T (s)")
>>> plt.ylabel("Count Rate (count/s)")
>>> plt.title("47 TUC")
>>> plt.show()

These commands produce Figure 3.

Figure 3: Comparing the lightcurves

[Thumbnail image: The lightcurves are plotted in a split window with a solid white line connecting cross symbols and no error bars.]

[Version: full-size]

[Print media version: The lightcurves are plotted in a split window with a solid white line connecting cross symbols and no error bars.]

Figure 3: Comparing the lightcurves

The top plot contains lightcurve "src1" and the bottom plot contains lightcurve "src2". The x-axis values (time) for the two plots are aligned.

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:

  1. This is not an instrumental 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 and on the same chip.

  2. 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 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. Addition information about dead times can be found in the Computing Average HRC Dead Time Corrections thread.

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% cat hrc_src.reg 
# Region file format: CIAO version 1.0
circle(16476,16294,19.5)

unix% punlearn dmextract
unix% pset dmextract infile="hrcf00461N005_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_001N005_dtf1.fits
unix% dmextract 
Input event file  (hrcf00461N005_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: 

unix% python

>>> from pycrates import read_file
>>> import matplotlib.pylab as plt
>>> 
>>> plt.subplots(2,1,sharex="col")
>>> plt.subplots_adjust(hspace=0.4)
>>> 
>>> plt.subplot(2,1,1)
>>> tab = read_file("hrc_lc.fits")
>>> dt = tab.get_column("dt")
>>> rate = tab.get_column("count_rate")
>>> erate = tab.get_column("count_rate_err")
>>> plt.errorbar(dt.values, rate.values, yerr=erate.values, 
    marker="o", color="red", mfc="black",mec="black", ecolor="grey")
>>> plt.xlabel(r"$\Delta$ T (s)")
>>> plt.ylabel("Count rate (count/s)")
>>> plt.title("3C 273")
>>> 
>>> plt.subplot(2,1,2)
>>> ee = tab.get_column("EXPOSURE")
>>> plt.plot(dt.values, ee.values,
    marker="o", color="red", mfc="black",mec="black")
>>> plt.xlabel(r"$\Delta$ T (s)")
>>> plt.ylabel("Exposure Time (s)")
>>> plt.show()

creates Figure 4.

Figure 4: HRC lightcurve

[Thumbnail image: The points of the lightcurve are plotted with a solid white line connecting white cross symbols and no error bars.]

[Version: full-size]

[Print media version: The points of the lightcurve are plotted with a solid white line connecting white cross symbols and no error bars.]

Figure 4: HRC lightcurve

The Dead Time Factor (DTF) was included when creating this lightcurve. Note that the final time bin has a very low exposure value (bottom plot). This leads to an anomolously high count rate (top plot).

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        11.0732902274
     2  64940447.3669999987        11.1508118948
     3  64941447.3669999987        11.0442979298
     4  64942447.3669999987        10.8239338975
     5  64943447.3669999987        11.0010478186
     6  64944447.3669999987        10.8625693020
     7  64945447.3669999987        11.1579373574
     8  64946447.3669999987        11.0144558786
     9  64947447.3669999987        11.0962446993
    10  64948447.3669999987        11.0444782872
    11  64949447.3669999987        10.8780703573
    12  64950447.3669999987        10.8946114585
    13  64951447.3669999987        11.0979602670
    14  64952447.3669999987        10.9051273434
    15  64953447.3669999987        11.0032931023
    16  64954447.3669999987        10.9659395796
    17  64955447.3669999987        11.1047841342
    18  64956447.3669999987        10.9752825787
    19  64957447.3669999987        11.2070096507
    20  64958447.3669999987        11.1219813983
    21  64959447.3669999987        51.5457065863

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 = acisf00953N005_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 = acisf00953N005_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 = hrcf00461N005_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_001N005_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)
25 Jun 2008 updated image display to place figures inline with text
12 Jan 2009 updated for CIAO 4.1: provided both Python and S-lang syntax for ChIPS
05 Feb 2010 updated for CIAO 4.2: ChIPS version; ObsID 461 file version and corresponding change to screen output
13 Jan 2011 reviewed for CIAO 4.3: no changes
11 Jan 2012 reviewed for CIAO 4.4: minor change in HRC lightcurve due to bug fix in dmextract (applying DTF file)
03 Dec 2012 Review for CIAO 4.5 ; added see also to glvary & dither_region thread.
03 Dec 2013 Review for CIAO 4.6. Added note about early data. Minor edits.
18 Dec 2014 Reviewed for CIAO 4.7; no changes.
17 Jul 2017 Updated figures to plot delta-T. Added additional links to dead-time thread. General cleanup.
02 Apr 2019 Update to use matplotlib to plot.
25 Jan 2022 Reviewed for CIAO 4.14. Updated for Repro-5/CALDB 4.9.6
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