Status of jet reconstruction and Higgs searches at LHCb
description
Transcript of Status of jet reconstruction and Higgs searches at LHCb
Status of jet reconstruction and Higgs searches at LHCb
M. Musy - 26th May, 2009
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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)
Associated productionof SM Higgs
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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
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
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
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
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Resolution ~14 mrad
Pt/GeV
jet1
WZ evtsttbarHiggs120
Jet 1Jet 2Jet 3
Pt/GeV
seed algorithm, cont’d
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
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
before calibration
after calibration (points)
Pt(
H)
/ P
t(Je
t1+
Jet2
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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
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
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
..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…
…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
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Ejet true generated Higgs mass
reco
n’ed
mas
seffect of saturation
raw mass
calibrated mass
90 GeV120 GeV140 GeV
90 GeV120 GeV140 GeV
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
- 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:
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
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
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.
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!
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)