erbox

erosita/erbox-1.7

Summary:

Detect sources using a modified sliding box algorithm.
Description:

The task ERBOX searches the input images for sources that are brighter than the expected background fluctuations at a given image position. A likelihood threshold can be specified with parameter likemin, where
 likemin = -ln (P)
Where P is the probability that the count value in the detection box is caused by a background fluctuation.
The background in a detection box is either take from the optional background map  bkgima_flag="Y" or
is interpolated from a frame shaped region around the  detection box.
The box size is specified by paramter  boxsize, the side length of the box is 2*boxsize+1.

When boxsize=4 (the default value), the counts in the box will be weighted with a beta function, adapted in width 
to the approximate eROSITA PSF.
The sensitivity for extended sources can be increased by running nruns iterations of the algorithm, where
the image is rebinned by a factor 2**(n-1) in each iteration, effectively increasing box size and width of the 
beta function by the same factor.







Parameters:

  
   images [="image.fits"] (string_array)  OPTIONAL  
    
      Input image(s)  
    
  
   boxlist  [="boxlist.fits"] (string)  OPTIONAL  
    
      Output source list file 
    
  
   expimages [="expimage.fits"] (string_array)  OPTIONAL  
    
      Exposure map(s)  
    
  
   detmasks [="detmask.fits"] (string_array)  OPTIONAL  
    
      Detection mask(s)  
	
    

   bkgimages [="bkgimage.fits"] (string_array)  OPTIONAL  
    
      Background maps(s)  
	
    

   emin [=500.] (real_array)  OPTIONAL  
    
       Minimum energies [PI channels] 
	
    
  
   emax [=2000.] (real_array)  OPTIONAL  
    
       Maximum energies [PI channels] 
	
    
  
   ecf [=1.0] (real_array)  OPTIONAL  
    
       Energy conversion factors 
	
    
  
   hrdef  (string)  OPTIONAL  
    
       Energy bands used for hardness ratios 
	
    
  
   nruns [=3] (integer)  OPTIONAL  
    
       Number of compression steps 
	
    
  
   likemin [=6.0] (real)  OPTIONAL  
    
       Minimum boxlist detection likelihood 
	
    
  
   boxsize [=4] (integer)  OPTIONAL  
    
       Detection box size (half_width-0.5) [pixel] 
	
    
  
  compress_flag[=N] (boolean)  OPTIONAL  
    
       Use memory saving image handling  
	
    
  


  expima_flag[=Y] (boolean)  OPTIONAL  
    
       Use exposure maps  
	
    
  


  detmask_flag[=Y] (boolean)  OPTIONAL  
    
       Use detection mask  
	
    
  

  bkgima_flag[=Y] (boolean)  OPTIONAL  
    
      Use background maps  
	
    
  





Input files:

images 
expimages 
detmasks 
bkgimages  


Output files:

boxlist



Examples:


Simultaneous detection on 2 input images in local mode:

  erbox images="image05-2keV.fits  image2-5keV.fits" \
        boxlist="boxlist.fits" \
        expimages="image05-2keV_expmap.fits  image2-5keV_expmap.fits" \
        detmasks="detmask.fits" \
        emin="500 2000" \
        emax="2000 5000" \
        hrdef="1 2" \
        ecf="1.0" \
        nruns=3 \
        likemin=6. \
        boxsize=4 \
        compress_flag="N" \
        bkgima_flag="N" \
        expima_flag="Y" \
        detmask_flag="Y"




Simultaneous detection on 2 input images in map mode:

  erbox images="image05-2keV.fits  image2-5keV.fits" \
        boxlist="boxlist.fits" \
        expimages="image05-2keV_expmap.fits  image2-5keV_expmap.fits" \
        bkgimages="image05-2keV_bkg.fits  image2-5keV_bkg.fits" \
        detmasks="detmask.fits" \
        emin="500 2000" \
        emax="2000 5000" \
        hrdef="1 2" \
        ecf="1.0" \
        nruns=3 \
        likemin=6. \
        boxsize=4 \
        compress_flag="N" \
        bkgima_flag="Y" \
        expima_flag="Y" \
        detmask_flag="Y"