1. Electron -converted photon –pi0 discrimination
Xiao Hong

2. About the number of tracks in single photon sample

3. If the number of tracks in single photon sample is larger than 0,these photon samples can be considered as converted photons.
We can analyze the single photon events which reconstructed no track to see if they are converted photons.

4. events have no reconstructed track （0 conv_pho：83%，1 ：17%）

5. About 74% of single photon events reconstructed no track.
In this part about 83% of events reconstructed no converted photon and about 17% of events reconstructed one converted photon.
So there are about 60% unconverted photons and 40% converted photons. It is reasonable.

6. Pi0 ele discrimination
Use the variable :SC energy over electron pt

7. Normalized to 1

8. Esc/pt
In electron sample most of Esc/pt is small. We can’t use the variable in no reconstructed electron case.
(try to use track information next time)

9. There are so less reconstructed electrons in single pi0 sample

10. Single electron sample

11. Pi0 ele Reco pho:0:90% 2:90% Conv pho:0:60% 0,1:10% 1:40% 2:70%
1:40% :70%
Reco ele:0:77.9% :15.6%(can’t discriminate, we can only use the left part)
1:19.6% :6.9%
2:1% :77.4%

12. In converted photon there are 95%which can’t reconstruct electron
Because the innermost hit is not in the first two layers.

13. The shape of pi0 is almost the same as photon (cut at 1)

14. electron

15. This is the plot after the layer cut is used, so we can see that we can improve their result.

16. deltaR of Electron and the closest reco photon(Blue:ele;red:pi0)

17. After the cut of layer<2 to see if we can improve the result of note
Ele : pi0:60885
(Eopt<3 )27965(69.2%) (38%)
(deltaR<0.1)27934(68.9%) (36.7%)
The first is more useful which can keep 90% signal and reject 21.7% background.

18. For the whole process: two main cuts
Ele: pi0:
(Layer<2)99.68% %
(eopt) % %(keep off 63.3%)

19. Next step
For variable Esc/pt :there are so less reconstructed electrons in single pi0 sample to use for discrimination, we can try to use track information. Because the first valid hit of a track doesn’t have to be in the first two layers.
It will be hard to see the difference in the plot directly.
My plot is different from the note’s,some of its cuts is not so good in my analysis.
Try to use TMVA to discriminate signal and background, so we can get the discrimination efficiency directly from the result of training and see if it is useful.
There is some problems with TMVA. I’ll show the result next time.