Four methods are available for obtaining the initial positions, selected using parameter MODE:
The results may optionally be written to an output positions list which can be used to pass the positions on to another application (see parameter OUTCAT), or to a log file geared more towards human readers, including details of the input parameters (see parameter LOGFILE).
The uncertainty in the centroid positions may be estimated if variance values are available within the supplied NDF (see parameter CERROR).
[search] [maxiter] [maxshift] [toler]
A comma-separated list of strings should be given in which each
string is either an attribute setting, or the name of a text file
preceded by an up-arrow character "
". Such text files should
contain further comma-separated lists which will be read and
interpreted in the same manner. Attribute settings are applied in
the order in which they occur within the list, with later settings
overriding any earlier settings given for the same attribute.
Each individual attribute setting should be of the form:
name
=
value
where
name
is the name of a plotting attribute, and
value
is
the value to assign to the attribute. Default values will be
used for any unspecified attributes. All attributes will be
defaulted if a null value (!) is supplied. See
Plotting Attributes
for a description of the available
attributes. Any unrecognised attributes are ignored (no error is
reported). [current value]
Successive estimates of the centroid position are made by using the previous estimate of the centroid as the initial position for another estimation. This loop is repeated up to a maximum number of iterations, though it normally terminates when a desired accuracy has been achieved.
The achieved accuracy is affected by noise, and the presence of non-Gaussian or overlapping features, but typically an accuracy better than 0.1 pixel is readily attainable for stars. The error in the centroid position may be estimated by a Monte-Carlo method using the data variance to generate realisations of the data about the feature (see parameter CERROR). Each realisation is processed identically to the actual data, and statistics are formed to derive the standard deviations.
KAPPA --- Kernel Application Package