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<H1><A NAME="SECTION00820000000000000000">&#160;</A>
<A NAME="theory">&#160;</A>
<BR>
Theory
</H1>
 ROMAFOT uses a linearised least squares analytical reconstruction method
 on two-dimensional data frames. Introductions of modern tests of 
 significance (e.g. <IMG
 WIDTH="54" HEIGHT="78" ALIGN="MIDDLE" BORDER="0"
 SRC="img179.gif"
 ALT="$\chi^{2}$">)
to measure the discrepancy between 
 observations and hypothesis can be found in many excellent text books 
 (see e. g. Bevington, 1969). Hence, it is not really useful to give 
 any theoretical summary here.

<P>
Although minimisation of the absolute deviations or, more precisely,
 the square of deviations between data and a parametric model is not 
 the only technique to derive optimum values for parameters, it is 
 generally used because it is straightforward and theoretically justified.
 There are no basic difficulties in extrapolating this method for the
 case of a model function with non-linear parameters if <IMG
 WIDTH="54" HEIGHT="78" ALIGN="MIDDLE" BORDER="0"
 SRC="img180.gif"
 ALT="$\chi^{2}$">
is continuous in parameter space. Problems may arise if local minima 
 occur for ``reasonable'' values of parameters and methods need to be 
 employed to search for absolute minima.

<P>
Nevertheless, if the PSF parameters are reasonably estimated one can
 assume that the localisation of the region of absolute minimum is 
 generally well determined. This is an important condition since the method 
 ROMAFOT uses requires that high order terms in the expansion of the fitting 
 function are negligible, a condition satisfied if the starting point 
 is close to the minimum.

<P>
This method is to expand the fitting function to first order in a Taylor
 expansion, where the derivatives are taken with respect to the parameters 
 and evaluated for the initial guess in this space. The calculation of the
 model parameters to fit the data has led to the solution of the usual 
 system:
 <BR><P></P>
<DIV ALIGN="CENTER">
<!-- MATH: \begin{displaymath}
\frac{\delta\,\Sigma}{\delta\,P_{j}} = 0,
\end{displaymath} -->


<IMG
 WIDTH="85" HEIGHT="52"
 SRC="img181.gif"
 ALT="\begin{displaymath}\frac{\delta\,\Sigma}{\delta\,P_{j}} = 0,
\end{displaymath}">
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<BR CLEAR="ALL">
<P></P>
where <IMG
 WIDTH="46" HEIGHT="54" ALIGN="BOTTOM" BORDER="0"
 SRC="img182.gif"
 ALT="$\Sigma$">
is the summation over all the <I>n</I> points of the square of 
 the difference between the model function and the data, and <I>P</I><SUB><I>j</I></SUB> is 
 the current parameter (
<!-- MATH: $j = 1,....,m$ -->
<I>j</I> = 1,....,<I>m</I>). As the method requires 
 an approximation of the function instead of the function itself, 
 the parameter adjustments the system reinstitutes are not exactly those 
 which correct the trial values, and the operations must be iterated.

<P>
Using the technique just outlined, ROMAFOT carries out stellar photometry 
 by fitting a sum of elementary Point Spread Functions, analytically 
 determined, to a two dimensional array. The sky contribution is taken 
 into account with an analytical plane, possibly tilted. This plane is 
 fitted (simultaneously) with the stars.

<P>
The analytical formulation of the PSF has obvious advantages in case
 of undersampled images allowing the comparison of the intensity of each 
 pixel to the <I>integral</I>  of the function over the pixel area. 
 
 ROMAFOT does not attempt to discriminate between stars and galaxies in 
 the phase of searching the objects. It prefers to take into account their 
 photometric contribution and delete them from the final catalogue by 
 checking the fit parameters with a suitable program. This program also 
 eliminates cosmic rays and defects, if present.

<P>
In several parts in this documentation reference is made to the sigma 
 (<IMG
 WIDTH="44" HEIGHT="54" ALIGN="BOTTOM" BORDER="0"
 SRC="img183.gif"
 ALT="$\sigma$">)
of either the Gaussian or the Moffat fitting function. In 
 both cases this sigma is <B>NOT</B> the sigma in the statistical sense 
 (i.e. the standard deviation) but the Full Width Half Maximum (FWHM) 
 of the distribution.  In case of the Moffet distribution, sigma is a 
 function of the parameter <IMG
 WIDTH="44" HEIGHT="72" ALIGN="MIDDLE" BORDER="0"
 SRC="img184.gif"
 ALT="$\beta$">,
i.e.  not equal to the FWHM except 
 for <IMG
 WIDTH="32" HEIGHT="39" ALIGN="MIDDLE" BORDER="0"
 SRC="img185.gif"
 ALT="$\beta=3.1$">.
This definition of sigma will be used throughout this 
 chapter. 

<P>
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<ADDRESS>
<I>Petra Nass</I>
<BR><I>1999-06-15</I>
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