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    User name Huber

    Log entry time 17:59:34 on March 29, 2004

    Entry number 454

    keyword=High -t test run; Regge model parameter constraint.

    I have looked at Runs 47740-47754, which is our set of high -t test runs taken near the end of the experiment. Using a fixed SOS momentum and angle corresponding to Q^2=2.5 GeV^2, epsilon=0.58, we scanned the HMS angle to obtain a -t-distribution from 0.28-2.1 GeV^2, at fixed W=2.0 GeV.

    My analysis made use of Tanja's latest ntuples, and corrections were made for randoms, dummy target, deadtime, and tracking efficiency. d^2sigma/dt/dphi were determined by normalizing to SIMC. For the lowest -t run, the normalized yield is within 3% of that predicted by the SIMC physics model (sig_blok). However, the physics model falls a factor of 1000 faster with t than the data, and this results in some rather large error bars on the cross sections. To get smaller error bars, the model would have to be modified to better describe the data distributions. For now, the attached plots should be considered as a constraint to any future modification of the SIMC physics model.

    Unlike the kinematics in Fpi-1, the VGL model predictions for the high-t data depend significantly on the value chosen for the rho-pi-gamma form factor. In PRC 57(1998)1454, VGL write "The rho mass scale is unknown and we use Lambda_rho2=Lambda_pi2(=0.462 GeV2) as a first guess." In this case, the Regge model falls far too fast with increasing -t. However, a value of approximately Lambda_rho2=1.5 GeV2 gives a t-dependence closer to that of the data. I have written to Michel Guidal about this, and here is his reply:
    "...I wouldn't be, a priori, shocked by a 1.5 GeV2 mass scale for the rho-pi-gamma form factor. For instance, the mass scale for the Kaon form factor and the K*-K-gamma forms factors that we derived in our Regge model (PRC61,025204,2000; PRC68,058201, 2003) for strangeness production are also of this order. I think you can safely go ahead with Lambda_rhopigamma=1.5 geV2,"
    However, the model seems to consistently under-estimate the data by about 40%. I'm not sure what this means. Perhaps Lambda_pi needs to be increased further. For now, I have stayed with the value in Jochen's thesis.




    A

    high-t_sig.ps

    r

    high-t_sig_r0p54.ps