Status of jet reconstruction and Higgs searches at LHCb
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Status of jet reconstruction and Higgs searches at LHCb
M. Musy - 26th May, 2009
p
p
beam jet 1
beam jet 2
q
q'
W
H
b
b
lept
on
neutrino
jet b
jet b
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Motivations
Jet reconstruction is important for searches of particles producing b-jets and displaced vertices, LHCb has a very good b-quark trigger and identification, and will be well calibrated with the large number of B mesons.
Standard Model Higgs of light mass going to bbar
Precision EW fits give MH = 126 +73/-48 GeV MH < 219 GeV at 95% C.L.
~~30% SM Higgs events 30% SM Higgs events are in LHCb acceptanceare in LHCb acceptance
MH=120 GeV
This is a field of search which is outside the main LHCb scope, still the potentialities of the detector are worth being investigated
SM predicts ~3 pb of HZ+HW at MH=120 GeV
→ about 100 evts in one year LHCb data (2/fb)
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Associated productionof SM Higgs
p
p
beam jet 1
beam jet 2
q
q'
W
H
b
b
lept
on
neutrino
jet b
jet b
Sig
ma
(pb)
H Mass (GeV)
Selection: isolated high Pt prompt lepton (Pt>15GeV, IP/<3) to tag W or Z
Reconstruction of 2 b-jets in the LHCb acceptance
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Nearness in angle Cone Algorithm.
Jet algorithms
2-seed algo Kt algo
22+ΔΔη=R
Reconstruction
Matching tracks and electromagnetic clusters
Use track and calorimetric information to build
4-MOMENTA
B-seed finding Kt jet algorithm
Cone algorithm with B-seed
B-jet tagging
B-jetsB-jets
2 B-seeds from 2-tracks sec. vertex
2 b-jets/eventcreated
For R=0.5~15 jets/eventreconstructed
2 tagged jets/event
Nearness in relative transverse momentum => Kt algorithm.
Cone jetKT jet
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Seed algorithm
1. Track selection: build a 2-track vertex using cuts on
P > 2 GeV
2 < 2.5 IP/ > 2.5, 2.8 (wrt Primary VertexPrimary Vertex) Pt >0.6, 0.8 KS candidates are excluded in the mass
window M=[0.490;0.505] GeV2. Seeds selection. Select seeds on the base of a
classifier using kinematic variables of tracks3. Double seeds selection. Pairs of seeds are
passed to a second classifier, pick up the best seeds pair candidate according to classifier output.
4. Jets construction. Other particles are added to form a jet if particle belongs to a cone of ΔR=0.7
Coming from bAll
Coming from bAll
seedB
seedA
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HW, HZ Monte Carlo events generated with MH=120 GeV and a lepton of Pt > 10GeV within the acceptance
no biases in the reconstructed jet direction
Theta of reco vertices
Th
eta
of tru
e v
ertic
es
Resolution ~14 mrad
Pt/GeV
jet1
WZ evtsttbarHiggs120
Jet 1Jet 2Jet 3
Pt/GeV
seed algorithm, cont’d
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Kt algorithm
• Need to identify the b-jet
A jet then is defined as a b-jet if it has at least 7 particles at least 2 tracks with impact parameter significance>2 at least 1 tracks with impact parameter significance>5 energy of charged particles > 3% of the total energy energy in a cone of R=0.4 around the jet axis>70% Ejet
A neural net also trained to discriminate against c-jets and light jets is also applied using kin. variables
cutSelection efficiency for b-jets 80% with good non-b jet rejection
• Standard algorithm used by many experiments
• in LHCb Kt algorithm is used to make jets in the event with R=0.75
b-jetsc-jetslight jets
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Calibration of jet energy
On a event-per-event basis: using MC information, take a few reference points in the mass spectrum and give each jet a momentum correction as a function of the Pt and rapidity of the jet such as to reproduce the peak. Samples used: WZ (91 GeV), H(120 GeV) and H(140 GeV) events
-> Fit for free parameters minimizing = (Mjj – Mx)2/N
Need to use more than one known mass point to avoid biases in the mass peak position..
Function chosen for the fit:
f(Pt, = a + b/(Pt –c) + d/
This factor is used to correct the momentum assuming Mjet = 5 GeV
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before calibration
after calibration (points)
Pt(
H)
/ P
t(Je
t1+
Jet2
)
Pt(Jet1+Jet2)
Pt/GeV
Calibration flattens out the reconstructed Pt distribution of H
Not yet perfect, especiallyat low Pt, but this regionis anyway less populated
f
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Jet 1Jet 2Jet 3
Pt(Jet) /GeVPt(Jet) /GeV
Jet 1Jet 2Jet 3
BEFORE calibration AFTER calibration
- average jet’s pt increase from ~35 to ~50 GeV- third jet is unchanged
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ttbar
beforeafter
beforeafter
GeV Mjj Mjj
91 65 23 94 23
120 83 31 122 31
140 94 37 139 33
raw corrected
Mjj /GeV
Mjj /GeV
Z, 91 GeV
Higgs, 120 GeV
Higgs, 140 GeVAlso tt background gets shifted
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..calibration of jet energy (cont’d)
Other possibility to calibrate: use MC information to fit directly the dependence of the jet reconstructed energy from the b-quark energy or from the visible energy in the detector.
- This can be done by means of a simple function:
Eje
t/E
b
- Or by means of a Neural Net which takes into account…
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…variables like:
- energies measured by the tracking and the calorimetry (ECAL, HCAL, PRSH, SPD, Muon chambers)- pseudorapidity of the jet- granularity and saturation of the calorimeter cells
Eje
t ca
lbra
ted
Ejet true generated Higgs mass
reco
n’ed
mas
seffect of saturation
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raw mass
calibrated mass
90 GeV120 GeV140 GeV
90 GeV120 GeV140 GeV
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Jet calibration from dataCalibration or jet energy correction can be based on pt balance between Z and reconstructed Jet in Compton-like scattering events:
2500 events per year with 2 muons of pt>10GeVand the b quark in LHCb acceptance [0;400mrad]
initial state radiation and final state radiation can be reduced by requirementson extra jet activity and
back-to-back jet-Z system
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- Preliminary selection with simple cuts gives an efficiency of 2 muons of about 61%- b-jet identification efficiency is about ~80%.- trigger efficiency: 88%- Jet reconstruction, 75% ~25% of the events with pt>10GeV
and the b in acceptance~620 events for 2/fb (1year)
Main limitation might be the available statistic. Studies are ongoing!
Use the same type of correctionfunction of Pt and pseudorapidity:
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Background studies
Sources of background [pb] handles to fight them:
Reducible bb 5.108 associated production tt 500 extra jet activity*/Z+jets 104
b jet identification W + jets 105
Irreducible ZW 6.3 di-jet mass resolution ZZ 2.5
Discrimination of a Higgs signal is a hard task due to various sourcesof background:
Search for sensitive variables and combine them into a NNet..
dominates
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Backgrounds, ttbar vs Higgs
..feed a nr of variablesinto the TMVA machinery..
ttbarHiggs 120 GeV
Neural Net output
ttbarHiggs
At pure MC level:
tt
Higgs
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Some performance figures
• Seed finding and jet reconstruction efficiencies
Higgs_120 ttbar
L0 trigger 79.5% 55.3%
Lepton 58.6% 38.2%
b-seeds 13.6% 7.1%
2 Jets 10% 4.4%
• Preliminary background estimation in the mass window 65 – 155 GeV gives a S/sqrt(B) ~ 0.2
• The expectation is O(30) reconstructed signal events in one year data taking (2/fb) for a 120 GeV mass Higgs
Room for improvements by using a NNet cut. Under study.
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Study of di-jet events
Estimated sensitivity
The dijet analysis of Minimum Bias events in LHCb can be a very effective tool for the of QCD predictions in a previously inaccessible range, even in the early runs of LHC.
Jets in the forward region also allows for detection of possible narrow resonances in models like:
Model: Randall-Sundrum graviton G* with k=0.01
…see G. Auriemma talk!
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Conclusion/Outlook
Very challenging attempt to reconstruct a SM Higgs at LHCb in the mass region up to ~135 GeV. At the moment the largely dominant background is tt. Work is focused in trying to optimize background rejection.
Different approaches for jet reconstruction and b-tagging have been developed. Jet calibration is necessary to discriminate against irreducible sources of backgrounds.
The high performance of LHCb in detecting secondary vertices and identify b-jets can be applied to any exotic particle decaying into 2 b quarks giving rise to high momentum jets in the high rapidity region. (e.g. Hidden Valley, MSSM with R-parity violation models)