Saturday, 21 July 2012

ROBFIT it works!

So how does it work - just realised I launched into GitHub loading without setting out how the code operates.

ROBFIT - that's the Fortran code used to find peaks within a complex spectrum, such as a gamma ray spectrum where the code originated. The idea behind the code is that it is designed to find the very smallest peaks (signals) in a spectrum and it does that by employing a ROBust FITing technique.

There are many spectral analysis packages on the market, however, these tend to require the spectrum to be broken into small sections, each of which is then fitted separately. This creates a couple of major problems. One is that if you have a large spectrum then there is substantial user intervention required resulting in fitting taking increased time to complete. Secondly, and more importantly, splitting the background into sections may misrepresent the background continuum. Small details in the spectrum could therefore be missed.

What's the solution?

Use ROBFIT - sorry - but yes do - the code gets round these problems by seperating spectra into two functions: background and foreground. The background contains slowly varying features and the foreground contains high-frequency content (peaks). Accurate separation of these functions allows the code to detect small peaks and decompose multiple-peak structures. ROBFIT iterates on background and foreground fitting to move smaller peaks from the background to the foreground.

A critical feature is that the code fits the background over the entire spectrum as a set of cubic-splines with adjustable knots - a knot being a place where two cubic splines meet. More on this in later post. Fitting over the whole spectrum range allows the background features to be continually fitted with fewer constants, resulting in a more accurate representation than is possible when fitting in small sections.

Two algorithms make operation this possible. The first is a data compression algorithm which uses a robust averaging technique to reduce the contributions to the background from peaks and spurious high points. The second is a minimisation algorithm (SMSQ routine) that minimises chi-square with respect to the constants of the background and foreground. With the background represented as a smoothly varying function, peaks can be identified as regions of the spectra that lie above this background curve - simples!

So now you know.....

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