1. Arnaud Duperrin (CPPM Marseille)
Jets+met Triggers
SM Higgs boson search in the HZbb final state
Arnaud Duperrin (CPPM Marseille)
on behalf of D0 and CDF
Alexandre Zabi (Oct. 04)
D0 France PhDs (on these topics)
Thomas Millet (May 07)
Fabrice Tissandier (Oct. 07)
Samuel Calvet (Sept. 07)
Bertrand Martin (Sept. 08)
Christophe Ochando (Sept. 08)
Florent Lacroix (Dec. 08)
TRIGGERS
Jets+MET Signals
Trigger Systems
Design
Historic
Data Trigger Efficiency
Performances
ANALYSIS
Data Sample
SM Backgrounds
The Multijet Background
Selection/Systematics
Results/Improvements
CDF

2. 1) Which Jets+met signals do we want to TRIGGER on?
It is challenging:

3. 2) Trigger System at D0 (online)
Full reco (offline)

4. Why an upgrade of the trigger system in 2006 ?
@2401030 cm-2 s-1
new hardware (faster)
new tools (ex MET)
new design (ex: Oring)
Run IIb:
31032 cm-2 s-1
Run IIa:
0.51032 cm-2 s-1
data (min bias cm-2 s-1

5. (arbitrary normalization)
3) Jet+MET trigger design: an example at L3
25 GeV
Signal ZH (mH=115 GeV)
(arbitrary normalization)
Result of the L3 design of Run IIb jets+met triggers for Higgs search  cut rate to tape by ~50% while keeping trigger efficiencies constant (~85%)
 Higgs and NP jets+met triggers are kept unprescaled up to highest luminosity

6. 4) Jet+MET trigger: design historic
6% 23% of
Fev. 2003
July 2003
June 2004
July.
2003
June.
2004
v11
v12
v13
Fev.
2003
MHT30 FH EM CC EC
Tower (TT)
Trigger Tower
L1: CJT(3,5) : 3 TT with ET>5 GeV
L2: MHT>20 GeV
L3: at least 1 one jet, MHT>30 GeV, HT>50 GeV 
improved the triggers as function of the instantaneous luminosity increases

7. design historic CC    June 2006 monjet+met dijet+met multijet+met
v15
Run IIb
monjet+met
dijet+met
multijet+met
38% 33%
June
2006
July
2005
June 2004
July 2005
June
2004
v13
v14
JT1_ACO_MHT_HT
JT2_MHT25_HT
Run IIb: Oring of several complex triggers  very different from the first “MHT30” trigger
(and I am skipping a lot of the technical difficulties which went into these designs…)
L1: MET>24 GeV and Jet Pt1>20 GeV and Jet Pt2>8 GeV and “no back-to-back jets” (noBB)
L2: Pt1>20 GeV, MHT>20 GeV, HT>35 GeV, noBB
L3: 2 Jets Pt>9 GeV, MHT>25 GeV, no BB (170o), (Jet1,MHT)>25o, MET>25 GeV CC  
L1JET CSWJT(1,8,3.2) 
(June 2006)

8. 5) Trigger Efficiencies
GEANT program does not simulate the D0 calorimeter response correctly
 need to calibrate the response of the simulated trigger system with the data
Jet 1
Jet 2
QCD data/MC: 2 jets back-to back
Offline is ~OK
TT calibration = bring the precision readout + shifting + smearing of TT energy to match data/MC
Two approaches:
1) Calibrate the online trigger simulator (called d0trigsim):
get the jet+met trigger response
takes the complex correlations between the objects (jets and MET)
allows to study the systematics
TT calibration
Results: shown for L1 Jets and L1 MET
DATA MC
before
after
data/MC comparison for L1 objects entering in the HZ triggers looks good after calibration (work in progress)

9. “at least one L1 jet with ET>30 GeV & ||<3.2”
Second approach: derive a standalone parametrization to “emulate” the jet+MET Higgs trigger response by calibrating objects directly and study possible remaining correlations (current choice for the analysis shown later)
Z+- +jets and W() + jets data:
equivalent to jets+met data from the calorimeter point of view
well understood signal and easy to collect (isolated muon trigger)
 triggers
+jets+MET triggers
(both are data)
Example on how to parametrize: term CSWJT(1,30,3.2) :
“at least one L1 jet with ET>30 GeV & ||<3.2”
Term efficiency
(+ complex Oring taken into account…)
6) Trigger Performances
+jets+MET triggers
“emulation”
(both are data)
HZ signal MC:
HZ Trigger efficiency: (for loose offline cuts)
L1: 88%
L1+L2+L3: 84%
with un-calibrated d0trigsim: 91%

10. SM Higgs boson search in the HZbb final state
ZHbb
WHlbb
ZHl+l-bb
 with WH, ZH is the most sensitive channel at low mass
 same final state than many NP particles (ex. sbottom, stop, LQ3)
BR(Zl+l-) 3%
BR(Z)20% (3 neutrinos flavors)
(this search is also sensitive to W(l)H signal events when the lepton is not reconstructed represents 40% of the signal sample)

11. 1) Data Sample 2) SM Backgrounds 90% 81%  Irreducible: Z()+jetsq W
Irreducible: Z()+jets
(800 pb)
reducible: W(l)+jets
(4500 pb)
(see Jean-Francois’s presentation on SM backgrounds + heavy flavors “scale factors”)

12. 3) The Multijet Background
Of the order of the milibarn (to be compared to signal cross section ~ pb mH=115 GeV)
multijet background contribution
jets:
Jets energy fluctuate  MET
jet 2
jet 1
MET
min(Jet,MET)
Selection:
2 or 3 jets Pt>20 GeV
MET> 50 GeV
min(Jet,MET), Aco veto…
…but difficult to simulate (from theory and instrumental point of view)  has to be evaluated with data (next slide)

13. R(jet 1, jet 2) MET Jet 1 Signal QCD sample sample (</2)
(>/2)
M_trkPt
M_trkPt
MET
Jet 2
SIGNAL
QCD
(TrkPt, MET)
is used to split the data in two samples: « QCD-like » and « signal-like »
R(jet 1, jet 2)
QCD
Z+jets
W+jets
 in the “signal sample” at preselection level, the SM+QCD contributions (QCD obtained from data) shows a good agreement between data/MC
Signal x 500

14. 4) Selection Dijet invariant mass Before After
Neural Network b-tagging:
4) Selection
Before
After
Dijet invariant mass
Signal (x10)
W+jets
W+saveurs
lourdes
Z+jets
Z+saveurs
Top
QCD
Signal (x500)
24 variables used:
dijet invariant mass(which is the most discriminant),
jets pT & ,
R(jet 1, jet 2), (jet 1, jet 2), etc…
Boosted decision tree (DT):
DT output

15. At mH=115 GeV, Ratio=7.5 observed (8.4 expected)
5) Systematics
trigger efficiencies:  5.5%
cross section:  6-16% (SM backgrounds),  6% (signal)
HF fraction:  50%
b-tagging efficiencies:  6%
Luminosity:  6.1%
At mH=115 GeV, Ratio=7.5 observed (8.4 expected)
6) Results
 most sensitive result for a low mass Higgs at D0
(predicted by the “SM” Higgs)
7) Improvements foreseen:
lower the MET cut down to 40 GeV  15% more signal (including trigger efficiencies)  work on trigger and QCD modeling
combine with “single b-tagging” and separate 2 & 3 jets bins
add an isolated track veto analysis
jet resolutions improvements
more luminosity!

16. At mH=115 GeV, Ratio=7.9 observed (6.3 expected)
8) CDF similar results (split by b-tagging categories + uses a NN to select the signal)
At mH=115 GeV, Ratio=7.9 observed (6.3 expected)
CDF improvements in sensitivity of VH->MET+bb analysis in the course of
win08
Single Tagged category adds ~10% to sensitivity.
Accept three jet events, where the 3rd jet is either a jet radiated off from a quark or a charged lepton => Adds sensitivity to WH->taunubb channel (hadronic  jets = 30% of selected signal events)
Multijet background shape and normalization are estimated from data => Multijet normaliztion uncertainty reduced to <20%.
Jet energies are corrected using tracking information => Improves Dijet Mass resolution.
Neural Network =>Improvement in signal acceptance with respect to cut-based selection

17. Conclusion
HZbb+
set one of the most stringent limits on Higgs boson production cross-section among various Tevatron searches
 many improvements still to come
 combination with other channels (see Gregorio’s presentation)
Jets + MET triggers are challenging but provide access to very important search channels (not only Higgs but also to SUSY)
with Fermilab, Manchester, Imperial College, among others
 Undoubtly a very useful experience acquired at Tevatron in a challenging but important area which can be expanded at LHC experiments