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eSASS task: ERBOX

eROSITA/erbox-1.18.1


Scope

This document provides a description of the algorithm and instructions for the correct usage of the ERBOX task, which is part of eSASS (the eROSITA Science Analysis Software System).

Description:

The task ERBOX is based on a sliding box algorithm to detect peaks in the input science images. The task can be run in two modes: In local mode the background is measured in a 2 pixel wide frame around the source box, in map mode the background is taken from the background maps supplied in the files bkgimages and averaged over the source box. If the (recommended) parameter boxsize=4 (9x9 pixel detection box) is set, the image is first smoothed using a kernel of the form

f(x,y)= [1 + ( (x-x0)2 + (y-y0)2) / rc2 ]-1.5

with rc=3.75 pixels (15 arcsec for the standard 4 arcsec bin size). For other values of boxsize the image is folded with the box profile. The smoothed image is searched for peaks, creating a preliminary source list. The significance of the peaks is measured by comparing the number of box counts ni with the expected background counts bi in each input image i using the (logarithmic) likelihood

Li = - ln PΓ (ni, bi)

where PΓ is the regularised incomplete Gamma function

PΓ(a,x)=0xe-tta-1dt0e-tta-1dt

This peak search can be repeated several times after rebinning the input images by a factor of 2x2, effectively doubling the box size. In the case of multiple input images (e.g., in different energy bands), a combined likelihood is computed using Fisher's method: the probability values Pi from n independent tests of the same null hypothesis are combined into a global likelihood value

L'=-2i=1nlnPi,

which follows a χ2 distribution with 2n degrees of freedom. The combined detection likelihood of a source can therefore be calculated as

Ldet=-ln(1-PΓ(n,i=1nLi))

The source list is filtered using a threshold on the combined likelihood. For the significant sources the following parameters are calculated for each input image and for the combined data set:

Input parameters:

Input files:

Output files:

Examples:

Simultaneous detection on 2 input images in local mode:
  erbox images="image_soft.fits  image_hard.fits" \
        boxlist="boxlist_local.fits" \
        expimages="image_soft_exp.fits image_hard_exp.fits" \
        detmasks="detmask.fits" \
        emin="500 2000" \
        emax="2000 5000" \
        hrdef="1 2" \
        ecf="2.0E12 1.0E12" \
        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="image_soft.fits  image_hard.fits" \
        boxlist="boxlist_map.fits" \
        expimages="image_soft_exp.fits image_hard_exp.fits" \
        bkgimages="image_soft_bkg.fits image_hard_bkg.fits" \
        detmasks="detmask.fits" \
        emin="500 2000" \
        emax="2000 5000" \
        hrdef="1 2" \
        ecf="2.0E12 1.0E12" \
        nruns=3 \
        likemin=6. \
        boxsize=4 \
        compress_flag="N" \
        bkgima_flag="Y" \
        expima_flag="Y" \
        detmask_flag="Y"