0
π
Converted photon and
discrimination based on H  
analysis
Outline
• converted photon and π0 selection from
samples
• TMVA training and test
• conclusion and outlook
I. converted photon and π0 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.
I. converted photon and π0 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~.
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.
II. TMVA training and test
ORCA AN-2008/063
CMSSW167
More than 10000 converted photon and pi0 candidates
used to normalize separately.
The two plots show that the variable is not so much powerful
to discriminate converted photon and pi0 in CMSSW.
TMVA training and test
ORCA AN-2008/063
CMSSW167
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.
II. TMVA input variables
II. TMVA input variables
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.
II. Background rejection versus
Signal efficiency for 20<Et<40
II. Background rejection versus
Signal efficiency for 40<Et<60
II. Background rejection versus
Signal efficiency for Et>60
II. π0 rejection efficiency
isolated and unconversion using
isConverted()
Et GeV
π0 rejection for 90% converted photon efficiency
20-40
35%
40-60
30%
60- 
27%
Importance of input variables for
photon ’s Et<20&& Et>40 in MLP
method
--- MLP
: Ranking result (top variable is best ranked)
--- MLP
: ----------------------------------------------------------------
--- MLP
: Rank : Variable
--- 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
--- MLP
:
5 : conpho_s9_D__conpho_s9_M_conpho_s1_M_conpho_s2_ : 1.572e+01
--- MLP
:
6 : conpho_asymmetry
--- MLP
:
7 : conpho_eoverp
--- MLP
:
8 : conpho_trks_pt_D_conpho_et
--- MLP
:
9 : conpho_distance
--- MLP
: ----------------------------------------------------------------
: Importance
: 2.173e+01
: 4.057e+00
: 6.011e-02
: 5.657e-03
: 3.049e-04
These information are the output of TMVA doing analysis.
Importance of input variables for
photon ’s Et<20&& Et>40 in BDT
method
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--- BDT
--- BDT
--- BDT
--- BDT
--- BDT
--- BDT
--- BDT
--- BDT
--- BDT
--- BDT
--- BDT
--- BDT
--- BDT
--- BDT
: Ranking result (top variable is best ranked)
: ---------------------------------------------------------------: Rank : Variable
: Variable Importance
: ---------------------------------------------------------------: 1 : conpho_et
: 2.056e-01
: 2 : conpho_cEE
: 1.726e-01
: 3 : conpho_cPP
: 1.231e-01
: 4 : conpho_eoverp
: 1.180e-01
: 5 : conpho_cEP
: 1.043e-01
: 6 : conpho_distance
: 9.007e-02
: 7 : conpho_asymmetry
: 7.397e-02
: 8 : conpho_s9/(conpho_s9-conpho_s1-conpho_s2) : 6.017e-02
: 9 : conpho_trks_pt/conpho_et
: 5.213e-02
: ------------------------------------------ ----------------------
Importance of input variables for
photon’s Et<20&& Et>40 in RuleFit
method
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--- RuleFit
--- RuleFit
--- RuleFit
--- RuleFit
--- RuleFit
--- RuleFit
--- RuleFit
--- RuleFit
--- RuleFit
--- RuleFit
--- RuleFit
--- RuleFit
--- RuleFit
--- RuleFit
: Ranking result (top variable is best ranked)
: ---------------------------------------------------------------: Rank : Variable
: Importance
: ---------------------------------------------------------------: 1 : conpho_et
: 1.000e+00
: 2 : conpho_cEE
: 9.653e-01
: 3 : conpho_cPP
: 6.561e-01
: 4 : conpho_asymmetry
: 5.565e-01
: 5 : conpho_eoverp
: 5.554e-01
: 6 : conpho_cEP
: 4.778e-01
: 7 : conpho_s9/(conpho_s9-conpho_s1-conpho_s2) : 2.851e-01
: 8 : conpho_trks_pt/conpho_et
: 2.266e-01
: 9 : conpho_distance
: 1.113e-01
: ----------------------------------------------------------------
Delta Separation of input variables for
photon ’s Et<20&& Et>40 in Likelihood
method
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--- Likelihood
--- Likelihood
--- Likelihood
--- Likelihood
--- Likelihood
--- Likelihood
--- Likelihood
--- Likelihood
--- Likelihood
--- Likelihood
--- Likelihood
--- Likelihood
--- Likelihood
--- Likelihood
: Ranking result (top variable is best ranked)
: ---------------------------------------------------------------: Rank : Variable
: Delta Separation
: ---------------------------------------------------------------: 1 : conpho_cEE
: 2.862e-03
: 2 : conpho_eoverp
: 2.750e-03
: 3 : conpho_cEP
: 9.890e-04
: 4 : conpho_et
: 6.840e-04
: 5 : conpho_trks_pt/conpho_et
: 6.840e-04
: 6 : conpho_asymmetry
: 6.840e-04
: 7 : conpho_cPP
: 5.726e-06
: 8 : conpho_distance
: -4.317e-04
: 9 : conpho_s9/(conpho_s9-conpho_s1-conpho_s2) : -1.249e-01
: ----------------------------------------------------------------
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.