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Re: Fwd: Kfit with timing



Hi Elke,

The only thing which still worries me a bit is the assumptions on the
error matrix for the tracking. I think we should show one or 2 example for
the kinematic fits, but should clearly point out that this is still a
point of concern.


    I'm not sure I understand the point you keep trying to make when you say "point of concern". You're basically saying if we don't understand our errors, then the procedure doesn't work. I completely agree with you and this was an issue with CLAS. A number of groups tried to build a consistent kinematic fitter and failed because the error matrix returned from tracking was wrong. It wasn't until Curtis and Mike Williams made a detailed, systematic study of these errors that it started to work. In retrospect, the study they did could've been done much earlier in CLAS' lifetime, but it just wasn't given priority. One thing which I've emphasized from the first talk I gave on this to the group last May, is that when we take data we will need to have a concentrated effort to understand our error matrix as returned by tracking.

    *Everything* we will do in GlueX will require a thorough understanding of the systematics and errors of both the detector and simulation, and this aspect of the analysis is no different. We shouldn't be trying to make these first Monte Carlo studies with a broken error matrix, because we would not expect to do analysis on real world data with a broken understanding of the detector. It definitely warrants a bullet in any of these talks, not so much to emphasize the problems, but to emphasize we know what we are doing and that we understand what studies we will need to do in the early calibration days of GlueX. The kinematic fit and it's usage should be considered no different from any other aspect of our PID or reconstruction (pattern finding, cluster algorithms) and we should expect as much effort to be put into it's understanding as anything else in the experiment.

   The fact that the effective usage of the kinematic fit relies on our understanding of the error matrix should be no more a "point of concern" than any any other part of the reconstruction.
 

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Matt Bellis
Carnegie Mellon University
(office)  412-268-6949
(cell)    412-310-4586
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