Adjusting the dcdrift.param

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Adjusting the dcdrift.param

• Y.C.
  Han
• April 25, 2007

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Outline

• Introduction
• Methods
• Results and Conclusion

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Introduction

• The signal of e e- generated in our K0
  production experiment at LNS makes us big
  troubles. However, it can also provide the useful
  information, such as its XT-Curves give the
  performance of charged particles in the Drift
  Chamber.
• I was doing such a work to get the e e-
  XT-Curve function in the last several days for
  the data of Jan2007.

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Method

• In the class of DCTrackHit, we can search the
  nearest hit of charged particles from cell anode,
  layer by layer, then construct the trajectory.
• The distance between the trajectory and the
  middle of cell anode is defined as caldl cm,
  and the drift time of second electrons is defined
  as dt ns. For an ideal uniform drift electric
  field, caldl linearly depends on the dt. That is
  also the case we assume in the first step
  analysis. However, the drift electric field is
  not ideally uniform, and considering the strong
  magnetic field. we must correct the linear
  function later. I use pol2 for correction.

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Method

• In fact, we define dl as the assumed dt
  function f0,
• f0v0dt
  (1)
• in which v00.005cm/ns,
• then residdl-caldl as the deviation. We slice
  the two dt-resid histogram and
• project the slices into resid axis, then fit
  the projection histogram. I use the
• Gaussian here.
• With these fitting parameters of all the slices,
  we can get a
• fitting function of dt depending on resid. It is
  named f-fit1,
• f-fit1p0p1dt p2dtdt
  (2)
• using the f-fit1, we correct the assumed dl to
  be the function f-correct1,
• f-correct1f0-f-fit1-p0(v0-p1)dt -p2dtdt
  (3)

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Method
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Method
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Method
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Method

• In place of f0 with f-correct1, we reanalysis
  the data, then do the loop
• described above.
• we do the loops several times, and get the
  better result with good
• resolution and mean value.

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Method
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Result and Conclusion

• Because the experimental conditions are
  changes, it is not a good idea to use a same
  parameter file for all the runs.
• I choose ten runs from the Jan2007 data,
  adjusting the parameters. The run number are 915,
  939, 966, 982, 1007, 1031, 1052, 1076, 1119, and
  1140. One can apply the parameters to the near
  runs correspondingly.

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Result and Conclusion
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Result and Conclusion
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Result and Conclusion

• one can get a conclusion that the FWHM Gaussian
  fit for the residdt projection is about 350µm
  for SDC, and 540µm for CDC.

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• That is all.
• Thanks for your attention!