1.  discrimination with converted photons
Nicolas Chanon, Zhang Zhen
Guoming Chen, Suzanne Gascon-Shotkin, Morgan Lethuillier
15/05/2009 – IHEP CMS weekly meeting
I – Converted photons selection
II – TMVA results

2.  discrimination with converted photons
I – Converted photons selection
Samples : mH=120GeV H->, QCD
Preselection at generator level : Et1>37.5 GeV, Et2>22.5 GeV (background only)‏
Selection at reconstructed level :
- Use of converted photon collection in CMSSW_1_6_12 and ask isConverted=1.
- ||<2.5.
- Events are selected if there is at least one photon with Et>40 GeV. TMVA is then applied to all photons which have Et>25 GeV
- Tracker ISO : No tracks with pt>1.5 GeV inside ΔR<0.3 around the direction of the photon candidate. We consider tracks with hits in at least two layers of the silicon pixel detector.
- Ecal ISO : Sum of Et of the ECAL basic clusters within 0.06<ΔR<0.35 around the direction of the photon candidate <6 GeV in barrel, <3 GeV in endcap. If one of the candidates is in endcap the other has to satisfy : Sum of Et of the ECAL<3
- Hcal ISO : Sum of Et of the HCAL towers within ΔR<0.3 around the direction of the photon candidate<6 GeV (5 GeV) in barrel (endcap)‏

3.  discrimination with converted photons
I – Converted photons selection
Contents of the converted photon collection :
Event N, converted photon collection :
- convphot 1 : SC 1 : track 1
- convphot 2 : SC 1 : track 2
- convphot 3 : SC 1 : track 3
- convphot 4 : SC 2 : track 4 and track 5
- convphot 5 : SC 2 : track 4 and track 6
- convphot 6 : SC 2 : track 5 and track 6
- convphot 7 : SC 3 : nTracks=0, isConverted=0
=> For each converted photon supercluster, how to select the best tracks ?

4.  discrimination with converted photons
I – Converted photons selection
Study of converted tracks. Goal : select the best tracks associated to each SC. Idea :
this time I tried to investigatge the number of valid hits for each track.
2trk Case
1trk Case
Ntrk>=2 :
Sg : 23%
Bg : 29%
Ntrk>=3 :
Sg : 64%
Bg : 62%

5.  discrimination with converted photons
I – Converted photons selection
Number of valid hits per track
1trk
signal
2trk
signal
1trk
background
2trk
background

6.  discrimination with converted photons
I – Converted photons selection
Selection of the best tracks among all converted photon tracks candidates :
- Previously, I was using EoverP closest to 1.
- I tried to select the tracks having Nhits minimum and maximum.
- Nhits maximum gives always worst TMVA results
- Nhits minimum improves slightly the 2 tracks case (but the 1track stay almost the same). This has the advantage to use only tracks variable, and don't use EoverP variable (which had no meaning for the 1track case).

7.  discrimination with converted photons
II – TMVA results
1 track case
Input variables

8.  discrimination with converted photons
II – TMVA results
1 track case
Correlation

9.  discrimination with converted photons
II – TMVA results
1 track case
Results

10.  discrimination with converted photons Ranking of the variables
II – TMVA results
1 track case
Ranking of the variables
MLP
Ranking result (top variable is best ranked)‏
Rank : Variable : Importance
1 : conpho_cPP : 1.636e+06
2 : conpho_cEP : 6.603e+03
3 : conpho_s9/(s9-s1-s2) : 4.476e+00
4 : conpho_ptoverjetpt : 1.540e+00
5 : conpho_r : 7.695e-01
6 : conpho_closestSC_dR : 4.042e-01
7 : conpho_dR_SCtrkclosest : 2.999e-03
nHits, d0 tried => no improvement

11.  discrimination with converted photons
II – TMVA results
2 track case
Input variables

12.  discrimination with converted photons
II – TMVA results
2 track case
Input variables
- EoverP removed : improve slightly
- dR_SCtrckclosest added
- sigmaPhi added
Y. Maravin

13.  discrimination with converted photons
II – TMVA results
2 track case
Correlations

14.  discrimination with converted photons
II – TMVA results
2 track case
Results

15.  discrimination with converted photons
II – TMVA results
2 track case
Results
MLP
Ranking result (top variable is best ranked)‏
Rank : Variable : Importance
1 : conpho_cPP : 1.689e+06
2 : conpho_cEP : 3.965e+03
3 : conpho_SigmaPhi : 4.776e+01
4 : conpho_pairsep : 7.381e+00
5 : conpho_ptoverjetpt : 4.076e+00
6 : conpho_s9/(s9-s1-s2) : 3.899e+00
7 : conpho_trk34overEt : 7.055e-01
8 : conpho_closestSC_dR : 3.035e-01
9 : conpho_dR_SCtrkclosest : 8.764e-02
nHits, d0 tried => no improvement

16.  discrimination with converted photons
Conclusions :
- Continued to study the converted tracks variables. NhitsMin can be an alternative to EoverP for the selection of the tracks
- Using dR_SCtrkclosest, 1 trk is now at 63% of background rejction, and 2 trk case at 60% (previously it was 50%).
- Probably too many input variables in the 2 track case
Perspectives :
- Continue looking at the other tracks variables (IP, chi2...)‏
- The ranking should be redone at each step once removed the last variable (very long !)‏
- Use Ted's likelihood to select the best converted photon per SC in the 2 tracks case.
- Apply the MVA to the Gamma+Jet samples.
- Check with MC truth info the proportion of real Pi0 in the background samples (later, with Nancy's implementation of conversion MC truth).
- Investigate the asymmetry variable pointed out by Anagnostou
- Divide samples in different pt bin