Abel 1068 Event-2D :
Some Basics


Back to Event-2D Demos page
 10/5/08
dd@space.mit.edu

This page shows the effects of the commands in a1068_basics.sl.
The command window text is given below along with select plots or images that are created.
The user-typed input is shown in bold and is assumed to be input at the "hydra> " prompt.
Commentary is shown in green.
Load in the user-defined data sets -- note the ability to use RADIUS in addition to other fits columns.
.source a1068_data
 Reading file:  ../Data/obs_1652/evt1_s3.fits
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 Reading file:  ../Data/obs_1652/evt1_s3.fits
 - - -
 - Data:                                     e2d_meta[id].;  e2d_data[id].;  e2d_view[id].
  id  Mission-grat-instrument    Date         2D-axes     ignore  prt  m src   events(k)   exp(ks)            name  
   1        CHANDRA-NONE-ACIS  2001.095            X,Y       0     -   -  -       74.24      26.8         ABELL 1068
>  2        CHANDRA-NONE-ACIS  2001.095  RADIUS,ENERGY       0     -   -  -       74.24      26.8         ABELL 1068
 - - -
Look at the datasets...
e2d_view_data(1);
   e2d_view_data ( [ -90 , 90 , 2.5 ] , [ -90 , 90 , 2.5 ] ); 
     total events displayed: 53420     e2d_view[e2di].scale = 555%
     bins w/events have 50%, 90%, and max numbers of:  4,  22,  626
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Looking at the Residual with no Model defined yet gives an idea of where the data are statistically above 0. The significant threshold, |chi| > 2 here, is set by the user for each dataset.
e2d_view_resid(1);
         blue     indicates:   chi  <  -2   [ model is much greater ]
        green     indicates:  -2  <  chi  <  0   [ model is greater] 
     yellow-green indicates:  0  <  chi  <  2   [ data is greater ]
         red      indicates:   chi  >  2   [ data is much greater ]
 - - -


e2d_view_data(2);
   e2d_view_data ( [ 0 , 90 , 2 ] , [ 0.4 , 8 , 0.1 ] ); 
     total events displayed: 51186     e2d_view[e2di].scale = 526%
     bins w/events have 50%, 90%, and max numbers of:  5,  57,  325
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For
dataset 2 the axes are RADIUS (left-right is 0 to 90 arc sec.)

and ENERGY (vertically from 0.4 to 8 keV.)
Load in and list the user-defined model which consists of Spectra and geometric Components.
.source a1068_model_3T
 Spectrum created from: temp_a1068_T15A10.par
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 Spectrum created from: temp_a1068_T32A05.par
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 Spectrum created from: temp_a1068_T50A05.par
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Evaluate the source model...
s3d_update;
      o check/update foreground NH ...
      o check/update spectra ...
      o check/update the component geometry ...
      o check/update opaque types ...
s3d_list_model;

       --- Source-3D Model ---

 Model name      : s3d model
 Distance        : 505000 kpc.               Date : 2001.000
 Foreground NH   : 0.022 x10^22 /cm^2
 Cube radius     : 100 arc sec  with 57 cells/radius, cell size of 1.75439 arc sec.
 - - -
 s3d_NHarf_method = 1  and 'slope = 3
 Nominal spectral grid : 1 A  to 40 A,  with delta-log of 0.0003
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 - Spectra:                                                                  s3d_spec[id].
  id    min - max es   ph/ks/100cm^2    file
   1    0.31 - 12.40       80248        temp_a1068_T15A10.par
   2    0.31 - 12.40     56740.6        temp_a1068_T32A05.par
   3    0.31 - 12.40     57041.4        temp_a1068_T50A05.par
 - - -
 - Components:                                                               s3d_comp[id].
  id         name   ignore   type       norm   ispec   vtype  velval   vturb    rndint          rndclr
   1         Inner     0     xray    0.000908    1      -1         0       0      0.3          [0,1,0]
   2        Middle     0     xray     0.00247    2      -1         0       0        2          [1,0,0]
   3         Outer     0     xray     0.00559    3      -1         0       0       15          [0,0,1]
   7      Core-HiT     0     xray    0.000491    3      -1         0       0      0.1        [1,0.5,0]
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 The s3d_ps user parameter structure values:
{ratio=1.4, ang=-42.5, beta=0.5, rbeta=12.6}
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Show the spectra and NH curve...
s3d_plot_spectrum(1);
 Graphics device/type (? to see list, default /XSERVE): CR
s3d_oplot_spectrum(1,2);
s3d_oplot_spectrum(2,3);
s3d_oplot_spectrum(3,4);

           

s3d_plot_nh;
Turn off the outer-most model component for the 3D visualization to see the inner part better. Also select view option 3 to do a slice of the model keeping only the -Z half-space. Rotate it and save the image.
s3d_ignore(3);
s3d_view_comps(3);
normalization took: 0.359509 sec




s3d_notice(3);
s3d_proj_comps;

This next routine, e2d_pars2fom, does the whole parameters-to-figure-of-merit process - essentially everything inside a "function evaluation" that a fitting routine would access.

The routine s3d_xray_photons generates Monte-Carlo photons from the current s3d model components and their assigned spectra.

The routine e2d_inst_xray takes the sky photons and creates detector photons - a very poor man's MARX. Ideally this will also do XMM, Suzaku, etc. 

e2d_pars2fom;
         Event2D:   parameters to FOM
   - update parameters                                                  [e2d_update_pars]
   - updating the model ...                                                  [s3d_update]
      o check/update foreground NH ...
      o check/update spectra ...
      o check/update the component geometry ...
      o check/update opaque types ...
   - Dataset 1: generating photons from the model...                   [s3d_xray_photons]
      o Inner: generating 40588 MC photons...
      o Middle: generating 72848 MC photons...
      o Outer: generating 167121 MC photons...
      o Core-HiT: generating 14679 MC photons...
      o Total generated =  295236
      o Total kept after NH/arf =  222941     ( 75.51 % )
             1: pass photons through instrument simulation...             [e2d_inst_xray]
             1: load simulation into model arrays                          [e2d_inst2mod]
   - Dataset 2: generating photons from the model...                   [s3d_xray_photons]
      o Inner: generating 40588 MC photons...
      o Middle: generating 72848 MC photons...
      o Outer: generating 167121 MC photons...
      o Core-HiT: generating 14679 MC photons...
      o Total generated =  295236
      o Total kept after NH/arf =  222799     ( 75.46 % )
             2: pass photons through instrument simulation...             [e2d_inst_xray]
             2: load simulation into model arrays                          [e2d_inst2mod]
   - calculate and show the figure of merit...                              [e2d_list_fom]
      o Data-Model 1,  chi^2 = 4009.8  from 4521 cells;   chi^2/cell = 0.886927;  sum(D)/sum(M) = 1.02079  +/- 0.00441654
      o Data-Model 2,  chi^2 = 1905.2  from 2106 cells;   chi^2/cell = 0.904656;  sum(D)/sum(M) = 1.02055  +/- 0.00451086
      o Total FOM = 5915
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Look at the Data, Model and Residual images...
e2d_view_resid(1);
         blue     indicates:   chi  <  -2   [ model is much greater ]
        green     indicates:  -2  <  chi  <  0   [ model is greater] 
     yellow-green indicates:  0  <  chi  <  2   [ data is greater ]
         red      indicates:   chi  >  2   [ data is much greater ]
 - - -
At Left: Color-coded residual image,
indicating where Data >> Model (red), where Data << Model (blue),
and where Data ~ Model to within statistics (green-black).
Make 1D and K-S comparison plots for dataset 1 of axis 1 = Sky X ...
e2d_plot_1d(1,1);

e2d_plot_ks(1,1);
      o Data-Model 1,  K-S on X: Max |diff| is 0.0277656  at -20.7805 arcsec
                             ks_test2 statistic of 0.0277656  gives p-value of 0
Look at the Data, Model, Residuals for dataset 2 -- the RADIUS - ENERGY image...
e2d_view_resid(2);
         blue     indicates:   chi  <  -2   [ model is much greater ]
        green     indicates:  -2  <  chi  <  0   [ model is greater] 
     yellow-green indicates:  0  <  chi  <  2   [ data is greater ]
         red      indicates:   chi  >  2   [ data is much greater ]
 - - -
Residual:
Data:

Model:
Make 1D and K-S test plots on dataset 2, axis 1 = RADIUS...
Nice agreement - someone tuned the parameters :-)
e2d_plot_1d(2,1);

e2d_plot_ks(2,1);
      o Data-Model 2,  K-S on RADIUS: Max |diff| is 0.0178073  at 12.5179 arcsec
                             ks_test2 statistic of 0.0178073  gives p-value of 1.17149e-11
Make 1D and K-S test plots dataset 2, axis 2 = ENERGY...
e2d_plot_ks(2,2); 

e2d_plot_1d(2,2); 
      o Data-Model 2,  K-S on ENERGY: Max |diff| is 0.0156341  at 1.55644 keV
                             ks_test2 statistic of 0.0156341  gives p-value of 4.39508e-09\