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Re: Questions about fitting in MarlinTPC [message #1980 is a reply to message #1977] Mon, 10 May 2010 23:57 Go to previous message
Messages: 125
Registered: July 2005
Location: CERN
Hello Christoph,


I think we should better do this again. Of course it's possible to write the fitter in a way that it can handle both kinds of model (straight line and helix).

The chi^2 fitter just happens to be able to do straight lines. For some studies it is necessary to fix the curvature and set it to a specific value, so I implemented this. If one sets it to 0 it's a straight line. I totally agree that we should have a dedicated straight line processor.


The "RowBasedHitFinder" calculates the right covariance matrix for

This is what I expected. So it's time we get a proper implementation of the chi^2 fitter.


Actually I can't really follow your idea of parametrising x and y errors in terms of drift distance z.
The uncertainty of the position measurement (x,y,z) is determined by the reconstruction algorithm, since these values are computed by it.

If you plot the residuals in xy and z against z this is the parametrisation you get. So it's an a posteriori estimate for the mean error (if the individual errors are not available). since the individual errors are there now we definitely should use them.


Seems to me, that we possibly should rework the fitting scheme a little.

Yes, we should to that. I am also not happy with it. I think it's too complicated and tangled. I discussed with several people but could not come up with something simpler that provided the functionality I wanted. We should discuss it in a separate thread.


Concerning the parameter error calculation

It should only be a few lines to read the covariance matrix from Minuit and fill it into the track. I admit I was just lazy looking it up when I still had problems with the convergence.

The same for dEdx: One just has to write a few lines, no principle problem. Maybe one can put a generic calculation into the base class so one does not have to copy it for all the implementations.


I somehow still have a little bit of trouble with Minuit working this way. If both the track finding works reliably and the hit errors are reasonably well described the fit should converge. We could think at some point about outlier rejection, downweighing, blabla.

Yes, once the pattern recognition works Minuit converges fine. And we definitely should have an outlier rejection. Maybe just do a double pass fit?



Martin Killenberg

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