1. Converted photon and π 0 discrimination based on H    analysis

2. Outline converted photon and π 0 selection from samples TMVA training and test conclusion and outlook

3. I. converted photon and π 0 selection Judge photon is converted in reco: :PhotonCollection: isConverted() Select the corresponding converted photon in ConvertedPhotonCollection: fabs (EoverP+pairCotThetaSeparation –1) minimum photon isolation: Et(  )>20 GeV, |η|<2.5 Ecal and Hcal isolation as done in cut-based analysis Compare to my last talk, there are two differences. One is that once a photon satisfy the above selection, it will be accept. The event don’t need to satisfy that it has two photons which pass the cut-based selection criteria. So more pi0 candidates are found. The other is that the converted photon which has 2 tracks is considered.

4. I. converted photon and π 0 selection Samples: /H120_gammagamma_gluonfusion/CMSSW_1_6_7-CSA07-1193937318/RECO 250,000 events /Jets_Pt50up/CMSSW_1_6_7-CSA07-1198935308/RECO 1.4 M events We will consider the converted photon in three bins separately. They are 20~40GeV, 40~60GeV, 60~ .

5. II. TMVA training and test Two new variables(E) introduced in CMS AN-2008/063. One is the asymmetry defined as the energy of the all basic clusters in one side of a line minus the energy of all clusters in the other side, within a cone of 0.3 around the photon candidate and normalized to its energy. The line in the eta phi plane connects the two impact points. The other is the distance between the impact points.

6. II. TMVA training and test More than 10000 converted photon and pi0 candidates used to normalize separately. ORCA AN-2008/063 CMSSW167 The two plots show that the variable is not so much powerful to discriminate converted photon and pi0 in CMSSW.

7. TMVA training and test The two plots also show that the variable is not so much powerful to discriminate converted and pi0 in CMSSW. Anyway, the two variables will be used in TMVA. ORCA AN-2008/063 CMSSW167

8. II. TMVA input variables

10. II. input variable correlation You can see the correlation between conpho_eoverp and conpho_trks_pt/conpho_et is –33 now for signal. This time more than 10000 converted photon and pi0 candidates are used separately.

11. II. Background rejection versus Signal efficiency for 20<Et<40

12. II. Background rejection versus Signal efficiency for 40<Et<60

13. II. Background rejection versus Signal efficiency for Et>60

14. II. π 0 rejection efficiency isolated and unconversion using isConverted() Et GeV π 0 rejection for 90% converted photon efficiency 20-4035% 40-6030% 60-  27%

15. Importance of input variables for photon ’s Et 40 in MLP method --- MLP : Ranking result (top variable is best ranked) --- MLP : ---------------------------------------------------------------- --- MLP : Rank : Variable : Importance --- MLP : ---------------------------------------------------------------- --- MLP : 1 : conpho_cEE : 5.687e+06 --- MLP : 2 : conpho_cPP : 6.521e+05 --- MLP : 3 : conpho_cEP : 1.761e+05 --- MLP : 4 : conpho_et : 2.173e+01 --- MLP : 5 : conpho_s9_D__conpho_s9_M_conpho_s1_M_conpho_s2_ : 1.572e+01 --- MLP : 6 : conpho_asymmetry : 4.057e+00 --- MLP : 7 : conpho_eoverp : 6.011e-02 --- MLP : 8 : conpho_trks_pt_D_conpho_et : 5.657e-03 --- MLP : 9 : conpho_distance : 3.049e-04 --- MLP : ---------------------------------------------------------------- These information are the output of TMVA doing analysis.

16. Importance of input variables for photon ’s Et 40 in BDT method --- BDT : Ranking result (top variable is best ranked) --- BDT : ---------------------------------------------------------------- --- BDT : Rank : Variable : Variable Importance --- BDT : ---------------------------------------------------------------- --- BDT : 1 : conpho_et : 2.056e-01 --- BDT : 2 : conpho_cEE : 1.726e-01 --- BDT : 3 : conpho_cPP : 1.231e-01 --- BDT : 4 : conpho_eoverp : 1.180e-01 --- BDT : 5 : conpho_cEP : 1.043e-01 --- BDT : 6 : conpho_distance : 9.007e-02 --- BDT : 7 : conpho_asymmetry : 7.397e-02 --- BDT : 8 : conpho_s9/(conpho_s9-conpho_s1-conpho_s2) : 6.017e-02 --- BDT : 9 : conpho_trks_pt/conpho_et : 5.213e-02 --- BDT : ------------------------------------------ ----------------------

17. Importance of input variables for photon’s Et 40 in RuleFit method --- RuleFit : Ranking result (top variable is best ranked) --- RuleFit : ---------------------------------------------------------------- --- RuleFit : Rank : Variable : Importance --- RuleFit : ---------------------------------------------------------------- --- RuleFit : 1 : conpho_et : 1.000e+00 --- RuleFit : 2 : conpho_cEE : 9.653e-01 --- RuleFit : 3 : conpho_cPP : 6.561e-01 --- RuleFit : 4 : conpho_asymmetry : 5.565e-01 --- RuleFit : 5 : conpho_eoverp : 5.554e-01 --- RuleFit : 6 : conpho_cEP : 4.778e-01 --- RuleFit : 7 : conpho_s9/(conpho_s9-conpho_s1-conpho_s2) : 2.851e-01 --- RuleFit : 8 : conpho_trks_pt/conpho_et : 2.266e-01 --- RuleFit : 9 : conpho_distance : 1.113e-01 --- RuleFit : ----------------------------------------------------------------

18. Delta Separation of input variables for photon ’s Et 40 in Likelihood method --- Likelihood : Ranking result (top variable is best ranked) --- Likelihood : ---------------------------------------------------------------- --- Likelihood : Rank : Variable : Delta Separation --- Likelihood : ---------------------------------------------------------------- --- Likelihood : 1 : conpho_cEE : 2.862e-03 --- Likelihood : 2 : conpho_eoverp : 2.750e-03 --- Likelihood : 3 : conpho_cEP : 9.890e-04 --- Likelihood : 4 : conpho_et : 6.840e-04 --- Likelihood : 5 : conpho_trks_pt/conpho_et : 6.840e-04 --- Likelihood : 6 : conpho_asymmetry : 6.840e-04 --- Likelihood : 7 : conpho_cPP : 5.726e-06 --- Likelihood : 8 : conpho_distance : -4.317e-04 --- Likelihood : 9 : conpho_s9/(conpho_s9-conpho_s1-conpho_s2) : -1.249e-01 --- Likelihood : ----------------------------------------------------------------

19. Conclusion G. Anagnostou used variables seem not very powerful in CMSSW. When we consider the converted photon with 2 tracks and events don’t satisfying the the cut-based analysis. The reject rate is lower. We need to improve the reject rate with new variables.