1 GAUSSCLUMPS Decompose a 3-dimensional spectral line data set into Gaussian shaped clumps

        The  GAUSSCLUMPS  algorithm  was  written to iteratively decompose a
    3-dimensional data cube of two spatial coordinates and one spectral  co-
    ordinate into a series of Gaussian shaped clumps.  GAUSSCLUMPS starts by
    fitting a 3-dimensional clump locally to the maximum of  the  input  lbv
    cube.  It  then  subtracts this clump from the cube, creating a residual
    map, and then continues with the maximum of this residual map.
     The procedure is repeated until a stop criterion is met, for instance
     when the maximum of the residual maps drops below the 3 sigma level.

    When fitting a Gaussian to the maximum, a modified chi-squared  function
    is minimized. Three "stiffness" parameters control the fitting, ensuring
    that a local clump is fitted and subtracted.

    GAUSSCLUMPS was developed by Stutzki & Guesten (1990, ApJ, 356, 513) who
    applied  it  to their M17SW data sets. It was lateron described and ana-
    lyzed in more detail by Kramer et al. (1998, A&A, 329, 249) who also ap-
    plied  it  to data cubes of various Galactic clouds.  The papers of e.g.
    Heithausen et al. (1998) and Simon et al. (2000) give more  examples  of
    the application of gaussclumps to large scale CO maps.

    The  general  recommendation is to make sure that the data is fully sam-
    pled. For the "stiffness" parameters, use the  initial  guess  of  1,1,1
    which  usually  works  out well. It helps a lot if the emission drops to
    near zero at the map edges. Otherwise GAUSSCLUMPS may find clumps  which
    are  partly  outside the mapped region.  Make sure that GAUSSCLUMPS does
    not decompose noise into  clumps,  for  instance  by  setting  a  3sigma
    threshold.  GAUSSCLUMPS  does always create clumps which are larger than
    the resolutions. However, the intrinsic, deconvolved  FWHMs  may  become
    very  small.  It is therefore recommended to select the clumps which are
    intrinsically larger than a certain fraction  of  the  resolution  (e.g.
    50%),  from  the output files, for further analysis and discard the rest
    which anyway only contains only a tiny fraction of the total mass.

    Gaussclumps has also been applied to dust continuum maps, i.e.  maps  of
    only  two  dimensions  by e.g. Mookerjea, Kramer et al. (2004, A&A, 426,
    119).  The input data were prepared by adding two adjacent,  empty  "ve-
    locity"  planes  to the original 2d data cube. This simple trick "fools"
    the algorithm to run without problems.

    The following files are created by gaussclumps: gaussclumps3d.out  show-
    ing the details of the decomposition into clumps step by step and gauss-
    clumps3d.list which lists the parameters of all clumps  found,  line  by
    line.

    Blanked pixels are ignored. The blanking value and tolerance are defined
    in the image header.

    For more  details,  please  contact  Juergen  Stutzki  (stutzki@ph1.uni-
    koeln.de) or Carsten Kramer (kramer@iram.es).

    The  meaning of some parameters is evident. Here, we offer some explana-
    tion for the not so obvious ones. The distributed gaussclumps.init  con-
    tains reasonable starting values.

2 INP_IMAGE$
        TASK\CHARACTER "input file" INP_IMAGE$

    The input cube should be ordered in lbv

2 RMS$
        TASK\CHARACTER "rms [K]" RMS$

    rms of the input cube

2 FWHM_BEAM$
        TASK\REAL"beam FWHM [m] or [s]" FWHM_BEAM$

    Angular resolution of the input cube in arcmin or arcsec, e.g. 13S

2 FWHM_START$
        TASK\INTEGER "initial guess [beam FWHM]" FWHM_START$

    Initial guess of the FWHM of the Gaussian clump to be fitted in units of
    the input cube resolution, e.g. 1.5.

2 STIFFNESS$
        TASK\INTEGER "stiffness S0[3]" STIFFNESS$

    Stiffness parameters control the fitting of the Gaussian  clump  to  the
    maximum of the map.

    S0[1]  biases the fit to stay below the observed intensity but stay near
    the observed intensities. S0[2] biases  the  fitted  amplitude  to  stay
    close to the observed maximum.  S0[3] biases the fitted position to stay
    near the position of the observed maximum. S0=1,1,1  are  good  starting
    values and are often sufficient.

2 THRESHOLD$
        TASK\INTEGER "threshold [K]" THRESHOLD$

    Stop  iterating  when  the  maximum  of the residual map drops below the
    threshold.

2 MAXCLUMPS$
        TASK\INTEGER "max number of clumps" MAXCLUMPS$

    Stop iterating when the maximm of number of clumps is exceeded.

2 CONTRAST$
        TASK\INTEGER "contrast [fraction of maximum]" CONTRAST$

    To be written by C.Kramer or J.Stutzki.

2 MININTEGRAL$
        TASK\INTEGER "minimum integral [fration of total]" MININTEGRAL$

    Stop iterating when the integral of the residual map drops below a frac-
    tion of the integral of the original input map.

2 APERTURE_FWHM$
        TASK\INTEGER "aperture FWHM [initial guess]" APERTURE_FWHM$

    The  width of the Gaussian function weighting the observed points in the
    vicinity of the maximum biases the fit to find individual clumps  rather
    than  an  averaged, smeared-out, broad clump. A small width introduces a
    bias to fit small-scale structure. The width is given in  units  of  the
    resolution of the input map.

2 APERTURE_LMTS$
        TASK\INTEGER "aperture cutoff [initial guess]" APERTURE_LMTS$

    To  speed-up computation, no data outside the aperture cutoff is consid-
    ered for the fit.

1 ENDOFHELP





