1. Report Jet-veto SF and Uncertainties Jet smearing
Lailin Xu, Haijun Yang, Bing Zhou
The University of Michigan

2. Outline Jet-veto SF and uncertainties Effects of Jet smearing on MET
MET with/without METUtility
WW/top/tautau background uncertainties

3. Jet Veto SF and Uncertainties
SF =eZZ(data)/ eZZ(MC) = eZ(data)/eZ(MC)
Control samples:
Data (p833, 4.7 fb-1) Z ee and Z mm
MC (MC11C): Z ll
ALPGEN, PYTHIA
Systematic uncertainties
Parton showing modeling (with diff. MC generator)
JES/JER (using ATLAS package to smear jet ET)

4. Jet-veto SF Results
SF = (both for ee and for mm) from Z MC
 compared to WW analysis: SF = 0.963
Systematic uncertainty
< 0.3% due to JES/JER
~ 5% due to different MC generator (Alpgen gives SF = 1.00; Pythia gives SF = 1.02)

5. Jet smearing effects on MET:ee
With jet smearing (for all the EM-jets by hand)
Compared to no jet smearing

6. Jet smearing effects on MET:mm
With jet smearing (for all the EM-jets by hand)
Compared to no jet smearing

7. MET with METUtility:ee
MET with the Tool
Compared to without tool

8. MET with METUtility:mm
MET with the Tool
MET without the Tool

9. WW/top/tautau background estimation method
formular:
are efficiency factor for ee and mm events
Here Z+jets is the DY contribution without Ztautau

10. Cut flow for ee (final 34 events)

11. Cutflow:mm (final 45 events)

12. Cut flow em (final numner 15)

13. estimated background after all cuts
data estimated
background
mc background
(WW/ttbar/tW/tautau) ee
6.4+/-1.8+/-0.020
4.7+/-0.4+/-0.69 mm
8.4+/-2.4+/-0.027
10.8+/-1.2+/-1.66

14. Error propagation in data driven method

15. back up

16. Compared to no jet smearing

17. With jet smearing for all EM jets (by hand)

18. Compared to no jet energy smearing