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Search for RPV SUSY through the LLE coupling

Search for RPV SUSY through the LLE coupling. Anne-Catherine Le Bihan / François Charles. RPV final sate Tau-Id with neural networks NN efficiencies on data Data selection & efficiencies First result on 246,7 pb-1. RPV final state : decay via coupling.

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Search for RPV SUSY through the LLE coupling

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  1. Search for RPV SUSY through the LLE coupling Anne-Catherine Le Bihan / François Charles RPV final sate Tau-Id with neural networks NN efficiencies on data Data selection & efficiencies First result on 246,7 pb-1

  2. RPV final state : decay via coupling Final state : 2 LSP decays with coupling : • 2 taus + 2 electrons • 3 taus + 1 electron • 4 taus => look for 2 isolated electrons and at least one hadronic tau AC Le Bihan

  3. Reminder : tau identification in hadronic final states • 3 main backgrounds: • QCD background : make use of 3 neural networks • 2 NN designed for taus with one associated track (w/ or wo/ EM3 subcluster) • 1 NN designed for taus with at least 2 associated tracks • (last version - Arnaud Gay http://ireswww.in2p3.fr/ires/recherche/dzero/tauid.html) • electrons : veto on candidates with an electron matched in an 0.3 cone • + use specific NN trained on Z->ee • muons : veto on candidates with a medium muon matched in an 0.3 cone • + cut on E(tau)-ECH_5x5(trk)/pT(trk) > 0.7 ( p14 certification - D0 note 4453 ) AC Le Bihan

  4. Z->  ->  enriched sample Enriched sample : muloose CSG skim processed with muon isolation filter (>=p14.05) : select events with one isolated muon and 1 tau candidate of opposite sign, (mu,tau)>2.7 + kinematical cuts ET(tower1)+ET(tower2)/ET Background sample : select events with a muon in a jet and a tau candidate of same sign (mu,tau)>0.7 Try to estimate the Z-> content by fitting the profile distribution (for all hadronic modes). AC Le Bihan

  5. tracks not attached to a tau in an 0.5 cone / all tracks Z->  ->  enriched sample Check fit on other NN input variables : Cluster isolation : pT(R=05)-pT(R=0.3)/pT(R=0.3) AC Le Bihan

  6. Electron and muon contamination for and decay modes Before cutting on : E(tau)-ECH_5x5(trk)/pT(trk) > 0.7 Before cutting on NN(Z->ee) : electrons faking hadronic taus AC Le Bihan

  7. tau estimate (decay mode ) : 405 tau estimate (decay modes and ) : 848 tau content and AC Le Bihan

  8. First estimate of NN efficienciesand profile cluster isolation NN( ) > 0.8 or NN( ) > 0.8 : MC : 541 events data : 410 events (data) : 48 %  (MC) : 64 % (data)/ (MC) : 76 % (QCD bkg) : 2.2 % AC Le Bihan

  9. Remaining electron contamination Use e+tau invariant mass to check the remaining electron contamination : e loose : emfrac >0.9 iso<0.15 e tight : emfrac >0.9 iso<0.15 trackmatchchi2prob#-1 HMX8<50 likelihood >0.3 • How many loose electrons give a tau ? => match loose electron to tau e(tight) + e(loose) invariant mass e(tight)+tau invariant mass => 7% of loose electrons give a tau AC Le Bihan

  10. Data sample - cut flow Preselection : 2EM CSG skim, fixed with d0correct v6 - v6-a Trigger : single EM and Di-EM Bad calo runs, bad met and ring of fire lumi-blocks removed Dupplicated events and bad sam files removed Luminosity : 246, 7 pb-1 • Cut flow : 1) 2 electrons (likelihood >0.3, HMX8<50), M(ee) > 18 Gev/c2 1) at least 1 hadronic tau (decay mode or ) outside the ICD region 3) M(ee) (81,101) 4) met/sqrt(set) > 1.5 AC Le Bihan

  11. RPV signal Points generated with susygen v03-00-43 , 133 = 0.003: • can be lighter than : • -> : 100 % • -> : 100 % • => higher tau final state multiplicity AC Le Bihan

  12. trigger efficiency folded into MC Use allmost all EM and Di-EM triggers , v8-v12 Use Ulla’s method to determine the turn-on function : -> compare MC/data for events with 2 electrons, 15<M(ee)<60, met<10 Freq((pT-12.2)/sqrt(pT*0.66)) pT of leading electron (Gev/c) AC Le Bihan

  13. electron data/MC efficiencies EC-EC CC use tag and probe method on tight electron + track invariant mass: _CC(data) : 81 %  _CC(MC): 86 % _EC(data) : 67 % _EC(MC): 93% Like lihood efficiency : use tag and probe method on tight electron + loose electron invariant mass: _CC(data) :89%  _CC(MC):97% _EC(data) :68% _EC(MC):65% AC Le Bihan

  14. Cut 1 - 2 electrons : M(ee) Use Anne-Marie’s smearing :  (E) =0.047*E Multi-jet QCD background evaluated by inverting the cut on tau-electron NN : select events with 2 electrons with NN> 0.05 (more tau ~ jet like) AC Le Bihan

  15. Cut 1 - 2 electrons - met/sqrt(set) After correction of the electron smearing : discrepancy needs to be understood … AC Le Bihan

  16. Data / backgrounds - cuts 1,2,3,4 AC Le Bihan

  17. RPV signal - cut 3 M(ee) met/sqrt(set) AC Le Bihan

  18. Conclusion and plans • first estimate of NN efficiencies for hadronic taus on p14 data • added cut to remove electron and muon contamination • Preselected data agree reasonably well with the expected background • check signal cuts on points above the LEP limit : m( )>103 GeV/c2 • understand better MC/data differences for NN efficiencies • understand the met difference MC/data • use matrix method to cross-check the QCD background estimate AC Le Bihan

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