ermldet

erosita/ermldet-1.15

Summary:

. Determine source parameters by fitting the calibrated PSF or a PSF convolved source model to science images.

Description:

The task ERMLDET determines source parameters for a list of input sources from the file specified by parameter boxlist . The task uses a PSF fitting algorithm to fit a model of the source (either the PSF or a convolution of the PSF with an extent model) to the spatial distribution of counts taken from the images specified by parameter images . The basic set of fit parameters are x,y-position, count rates in each input image, and a source extent parameter. The fit parameters and derived quanitities are written to the output source list specified by parameter mllist. Fitting of the source positions and source extent is optional and is controlled by the logical parameters fit_position and fit_extent . If shapelet_flag=no, the relevant PSF is read from the corresponding calibration image file, the relevant PSF calibration image is chosen using the header information in the science images and the energy interval given by emin and emax . In the case of shapelet_flag =yes, the SHAPELIB library routines are used to construct a source specific PSF, which is based on the positions and energies of the photons contributing to the respective source. This option can only be used, if the input files ( images ) contain both image and events extensions, as provided by the EVTOOL task. The use of the SHAPELIB library requires input images that contain the corresponding event lists and attitude tables as additional FITS extensions (as optionally written by task EVTOOL.

Algorithms:

The input source list is processed sequentially in descending order of likelihood from column like . Neighbouring sources can also be fitted simultaneously as source clusters, the compilation of the source clusters is controlled by the parameters nmaxfit (maximum number of sources) and multrad (search radius).

After each model fit to a source or source cluster, the model is added to the background map. The final background + model maps can be written to the fits files srcimages.

As source model either a calibration PSF model or a PSF convolved extent model can be used:

fitext_flag=no : m(x,y) = PSF(x,y)

fitext_flag=yes : m(x,y) = PSF(x,y) ⊗ ext(x,y)

extentmodel=beta: ext(x,y) = (1+(x^2+y^2)/rcore^2)^(-1.5)

extentmodel=gaussian: ext(x,y) = exp(-(x^2+y^2)/sigma^2)

Parameters:

Input files:

Output files:

Examples:



ermldet mllist=mllist.fits \
        boxlist=mboxlist.fits \
        images="image_soft.fits image_hard.fits" \
        expimages="image_soft_exp.fits image_hard_exp.fits" \
        detmasks="detmask.fits" \
        bkgimages="image_soft_bkg.fits image_hard_bkg.fits" \
        emin="500 2000"\
        emax="2000 5000" \
        hrdef="1 2" \
        ecf="2.0E12 1.0E12" \
        likemin=8. \
        extlikemin=10. \
        cutrad=15. \
        multrad=15. \
        extmin=1.5 \
        extmax=30.0 \
        bkgima_flag="Y" \
        expima_flag="Y" \
        detmask_flag="Y" \
        extentmodel="beta" \
        thres_flag="N" \
        nmaxfit=3 \
        nmulsou=1 \
        fitext_flag=yes \
        srcima_flag=yes \
        srcimages="image_soft_src.fits image_hard_src.fits" \
        expima_flag=yes \
        detmask_flag=yes \
        shapelet_flag=no \
        photon_flag=no \
        sixte_flag=no