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The following gives a quick overview of the Event-2D software capabilities and use. See the comments in the example .sl files linked here (as .sl.txt) for more details. The Documenation page gives tips on getting help with the various routines and common variables.
  (click for fullsize) | The X,Y (and Energy) event data can be loaded at the command line with a simple custom
script. 1D projections can be plotted for each image axis and for the energy. [linux] hydra Files used: e0102_acis_data.sl, obs_3545_evt1.fits, obs_3545_extract.warf |
  
| The model components and their geometry and spectra are defined with a
custom script which sets-up and makes-use-of the Source-3D
infrastructure. Once defined, the model parameters (the norms and the values in the
parameter structure, s3d_ps) can be changed and the model updated
and viewed again.
. . . Additional files: e0102_model.sl |
   
| The Event-2D command e2d_pars2fom carries out the whole process to go
from the current model parameter values to the final Figure
of Merit (FOM) between model and data; this is shown schematically in the Big
Picture.
(The model components are updated, model simulated events are generated,
model and data events are binned, and the FOM is calculated.)
Relevant statistics are shown on the screen
and the data/model/residual images can be see
using e2d_view_resid. Model parameters can be manually
changed as above and then doing e2d_pars2fom will show
the effect on the FOM.
[linux] hydra Additional files: none. |
    (click for fullsize) | The process of changing a parameter value and then seeiing how the FOM
changes is supported through the e2d_scan_par routine and
associated infrastructure. The following example shows the norm of the ring
component being scanned.
[linux] hydraWhen the scan is complete a plot of the FOM vs the scan value is made. Because multiple evaluations are made at each scan value (the "3" argument) it is possible to estimate the variation of the FOM due to the Monte Carlo model/evaluation, i.e., the red and blue curves on the plot and the "+/- 33.2813" in the output. Additional files: scan_demo.sl |
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| To determine the best-fit values for a set of parameters we use a simple
conjugate-gradient scheme,
see the Statistics & Fitting & MCMC section of the
Event-2D overview.
The scanning mechanism above serves as the basis of the fitting
because the main step in the CG scheme is minimization along a 1D path (scan) in parameter space.
The mapping between the fit parameters, pvals[], and the user's model
parameters is defined and then starting values and smand ("should make a
noticable difference") values are set and the fit is carried out.
The fitting process is monitored and the steps are recorded and visible
through an HTML file which links to the various scans made during fitting.
As an example, see: cg_log_Mon_Oct_29_13_07_33_2012.html. [linux] hydra Additional files: fit_demo.sl |
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| The fitting above quickly found values for the model center,
xc & yc,
but other parameters like Ri & Ro,
seem to have a more complex and shallower minimum-FOM region.
For cases like this it is useful to use MCMC techniques to map out the low-FOM
regions in parameter space.
The MCMC exploration routine directly uses the same setup and infrastructure as the fitting, allowing MCMC to directly follow fitting if desired. Likewise the "fomlog" plots are made the same way and provide a useful way to see how the MCMC is behaving, for example the FOM vs iteration plot. The useful "output" of the MCMC are plots in parameter space showing the locations of the accepted MCMC points (red) and the low-FOM values (circles), e.g., for Ri & Ro. [linux] hydra Additional files: mcmc_demo.sl |
| Send comments to dd@space.mit.edu | above last updated 10/30/2012 |