Status of jet reconstruction and Higgs searches at LHCb

1. beam jet 1 lepton b p neutrino W q q' H p jet b b beam jet 2 jet b Status of jet reconstruction and Higgs searches at LHCb M. Musy - 26th May, 2009

2. 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 ~30% SM Higgs events are in LHCb acceptance Precision EW fits give MH = 126 +73/-48 GeV MH < 219 GeV at 95% C.L. MH=120 GeV SM predicts ~3 pb of HZ+HW at MH=120 GeV → about 100 evts in one year LHCb data (2/fb) This is a field of search which is outside the main LHCb scope, still the potentialities of the detector are worth being investigated

3. beam jet 1 lepton b p neutrino W q q' H p jet b b beam jet 2 jet b Sigma (pb) Associated productionof SM Higgs • Selection: isolated high Pt prompt lepton (Pt>15GeV, IP/s<3) to tag W or Z • Reconstruction of 2 b-jets in the LHCb acceptance H Mass (GeV)

4. 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 KT jet Cone jet B-jets B-jets Jet algorithms Nearness in angle • Cone Algorithm. Nearness in relative transverse momentum => Kt algorithm. 2 B-seeds from 2-tracks sec. vertex For R=0.5~15 jets/eventreconstructed 2 tagged jets/event 2 b-jets/eventcreated Kt algo 2-seed algo

5. seedA seedB Seed algorithm Track selection: build a 2-track vertex using cuts on P > 2 GeV 2 < 2.5 IP/ > 2.5, 2.8 (wrt Primary Vertex) Pt >0.6, 0.8 KS candidates are excluded in the mass window M=[0.490;0.505] GeV Seeds selection. Select seeds on the base of a classifier using kinematic variables of tracks Double seeds selection. Pairs of seeds are passed to a second classifier, pick up the best seeds pair candidate according to classifier output. Jets construction. Other particles are added to form a jet if particle belongs to a cone of ΔR=0.7 Coming from b All Coming from b All

6. seed algorithm,cont’d HW, HZ Monte Carlo events generated with MH=120 GeV and a lepton of Pt > 10GeV within the acceptance jet1 q Theta of true vertices WZ evts ttbar Higgs120 Jet 1 Jet 2 Jet 3 Resolution ~14 mrad Pt/GeV Pt/GeV Theta of reco vertices no biases in the reconstructed jet direction

7. Kt algorithm • Standard algorithm used by many experiments • in LHCb Kt algorithm is used to make jets in the event with DR=0.75 • 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 b-jets c-jets light jets Selection efficiency for b-jets 80% with good non-b jet rejection cut

8. 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 c2= S(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, h) = a + b/(Pt –c) + d/h This factor is used to correct the momentum assuming Mjet = 5 GeV

9. Pt(H) / Pt(Jet1+Jet2) before calibration after calibration (points) Pt(Jet1+Jet2) f h Pt/GeV Calibration flattens out the reconstructed Pt distribution of H Not yet perfect, especially at low Pt, but this region is anyway less populated

10. BEFORE calibration AFTER calibration Jet 1 Jet 2 Jet 3 Jet 1 Jet 2 Jet 3 Pt(Jet) /GeV Pt(Jet) /GeV - average jet’s pt increase from ~35 to ~50 GeV - third jet is unchanged

11. raw corrected before after Z, 91 GeV ttbar Higgs, 120 GeV before after Mjj /GeV Higgs, 140 GeV Also tt background gets shifted Mjj /GeV

12. ..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: Ejet/Eb - Or by means of a Neural Net which takes into account…

13. …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 effect of saturation Ejet calbrated recon’ed mass Ejet true generated Higgs mass

14. 90 GeV 120 GeV 140 GeV raw mass 90 GeV 120 GeV 140 GeV calibrated mass

15. Jet calibration from data Calibration 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>10GeV and the b quark in LHCb acceptance [0;400mrad] initial state radiation and final state radiation can be reduced by requirements on extra jet activity and back-to-back jet-Z system

16. 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) Use the same type of correction function of Pt and pseudorapidity: Main limitation might be the available statistic. Studies are ongoing!

17. Background studies Discrimination of a Higgs signal is a hard task due to various sources of background: Sources of background [pb]handles to fight them: Reducible bb 5.108associated production tt 500 extra jet activity */Z+jets 104b jet identification W + jets 105 Irreducible ZW 6.3 di-jet mass resolution ZZ 2.5 dominates Search for sensitive variables and combine them into a NNet..

18. Backgrounds, ttbar vs Higgs ttbar Higgs 120 GeV ..feed a nr of variables into the TMVA machinery.. At pure MC level: ttbar Higgs Higgs tt Neural Net output

19. Some performance figures • Seed finding and jet reconstruction efficiencies • Preliminary background estimation in the mass • window 65 – 155 GeV gives a S/sqrt(B) ~ 0.2 Room for improvements by using a NNet cut. Under study. • The expectation is O(30) reconstructed signal events in one year data taking (2/fb) for a 120 GeV mass Higgs

20. Study of di-jet events 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 Estimated sensitivity …see G. Auriemma talk!

21. 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)