1. Adjusting the dcdrift.param
Y.C. Han
April 25, 2007

2. Outline
Introduction
Methods
Results and Conclusion

3. 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.

4. 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.

5. In fact, we define dl as the assumed dt function f0,
Method
In fact, we define dl as the assumed dt function f0,
f0=v0×dt (1)
in which v0=0.005[cm/ns],
then resid=dl-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-fit1=p0+p1×dt +p2×dt×dt (2)
using the f-fit1, we correct the assumed dl to be the function f-correct1,
f-correct1=f0-f-fit1=-p0+(v0-p1)×dt -p2×dt×dt (3)

6. Method

7. Method

8. Method

9. 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.

10. Method

12. 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 One can apply the parameters to the near runs correspondingly.

13. Result and Conclusion

14. Result and Conclusion

15. Result and Conclusion
one can get a conclusion that the FWHM Gaussian fit for the resid%dt projection is about 350µm for SDC, and 540µm for CDC.

16. Thanks for your attention!
That is all.
Thanks for your attention!