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Semileptonic tt decays with 0.1/fb

Semileptonic tt decays with 0.1/fb. Stefan Kasselmann III. Physikalisches Institut B, RWTH Aachen. LHC/CMS schedule. Picture of the Tracker Inner/ Outer Barrel from July 2006. CMS closes at 31/08/07

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Semileptonic tt decays with 0.1/fb

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  1. Semileptonic tt decayswith 0.1/fb Stefan Kasselmann III. Physikalisches Institut B, RWTH Aachen

  2. LHC/CMS schedule Picture of the Tracker Inner/Outer Barrel from July 2006 • CMS closes at 31/08/07 • First beam: November 2007: - 0.9 TeV (CM) - 43 vs. 43 bunches - 1028-1030 cm-2s-1 • Debugging machine/detector • Then: Commissioning of all 8 sectors for full energy in winter 2008 shutdown • First Physics: Spring 2008 • - 14 TeV - 156 vs. 156 b. - 1032 cm-2s-1 • 0.1/fb : “A few weeks of data taking" http://lhc-commissioning.web.cern.ch 1/19

  3. “1st physics run” scenario • 0.1/fb corresponds to about 48.800 ttbar (inclusive) events, taking the LO cross section from PYTHIA (CTEQ 5L) (http://cmsdoc.cern.ch/cms/PRS/gentools/www/xsec/cmsxsec.html) • This analysis is (so far) based on the following assumptions: • no pixel detector  No b jet tagging were used! • no ECAL endcaps  Electron identification only in || < 1.47 •  No cut on MET used • Ideal: Use new MC data (CMSSW) in this specific detector configuration: • Tracking algorithms work different without pixel detector (seeds)! • Less material in front of silicon detector (particle interactions) • … • So far: Data (Pythia) from 2004 used (with pixel). Ongoing: Converting data files (ALPGEN) which better simulates gluon radiation processes • Goal: Develop analysis to "see" tops in this scenario (e.g. invar. mass spectrum) 2/19

  4. CMS detector y Electromagnetic calorimeter (ECAL) Myon chambers x   Silicon Tracker(Pixel+Strip) z Hadroniccalorimeter (HCAL) Superconductive coil (4 Tesla) Forward calorimeter 3/19

  5. tt production: produced via two processes (strong interaction): 87%: 13%: top pairs @ LHC • 10 top pairs/s @ 1034 cm-2s-1 • But: About 20 pile up events! • Main background: W+jets, Z+jets, Dileptonic ttbar decay 4/19

  6. BR(tt bW+bW-) ~ 100% BR(W+W- l11l22) ~ 11% BR(W+W- q1q2q3q4) ~ 44% BR (W+W- q1q2l) ~ 44% W+  u , d / c , s (3 colours) W-  u , d / c , s (3 colours) Top pair decay 2/3 1/3 (9/81)(36/81)(36/81) 5/19

  7. R = 0.2 Lepton identification • Only electrons(pT > 10 GeV, || < 1.47)and muons(pT > 10 GeV, || < 2.4) are used • Electrons: Likelihood based selection of electrons from candidates • Muons: Are taken as they come out of the GlobalMuonReconstructor • Lepton isolation consists of calorimeter and tracker isolation • For both a cone of R = srqt(2 + 2)=0.2 is used around the track of the particle • Calorimeter isolation: No energy deposits > 15% oflepton energy • Tracker isolation: No tracks > 10% of lepton momentum • Some of the input variables for electron likelihood: • E / P: super cluster energy / track momentum (for electrons close to 1) • H / E: energy in HCAL (behind super cluster) / super cluster energy (for electrons close to 0) •  = | SC - track | : Difference between super cluster position and extr. track pos. at ECAL • E9 / E25 : ECAL energy 3x3 cell / 5x5 cell • … 6/19

  8. Generic preselection Typical preselection: • L1 & HLT Trigger • 4 jets with pT > 10 GeV, || < 2.5 (low pT cut to be able to run different scenarios) • At least one (tracker & calo) isolated lepton with pT > 10 GeV 7/19

  9. Selection • First selection cut:Exactly one lepton • Less efficient cuts (not used): • - Two leptons with diff. charge • - Two lepton mass (Z peak) • The „one lepton cut“ is most efficient against Z+jets and dileptonicTTbar events • Z+jets suppression: 35% • Dileptonic suppression: 23% • Signal loss: < 1‰ W+jets Z+jets dileptonic logarithmic scale! • In about 98.1% of the selected semileptonic events the lepton taken is the one from W decay (that means it matches the MC signal lepton with R < 0.01 and has correct charge) 8/19

  10. Selection • Second selection cut:3rd jet pT > 45 GeV • Tried many cut variations on the (pT sorted) four highest pt jets • Most efficient against W+jets and dileptonicttbar events (which only have two high energetic b jets from hard interaction) • Dileptonic suppression: 67% • W+jets suppression: 99,9% • Signal loss: 40% W+jets dileptonic • In addition all other jets (4th, 5th) in the event must fullfill: pT > 30 GeV to reduce the jet combinations for the Jet Parton Matching (JPM) 9/19

  11. Selection • Third selection cut:Circularity > 0.3 • This variable has small values for planar events and high values for circular events. • This cut is most efficient against QCD events • QCD suppression: 99% • W+jets suppression: 45% • Signal loss: 40% • (But: Low statistics of QCD!) xy l • Result: After these three selection cuts one gets an S/B of about 0.9 • Now one has to find the three jets from top out of 4 or 5 jets. Therefore a likelihood was developed. 10/19

  12. Selection overview • 4 or 5 jets with pT > 30 GeV, 3rd jet pT > 45 GeV (pT sorted) • Exactly one (tracker & calorimeter) isolated lepton with pT > 10 GeV • Circularity > 0.3 p r e l i m i n a r y 11/19

  13. Jet Parton Matching • For early top physics JPM, I use six (simple) variables which distinguish between right and wrong jet pairings, namely angles, masses and pT of jets. • JPM criteria (All 4-jet-combinations out of 4 or 5 jets are used) • The sum of R(jet, parton) of all 4-jet-comb. is calculated, the lowest taken • ( -> best global matching) • Each jet then must fulfill: R(jet, parton) < 0.25 and • |PTMC – PTRec| / PTMC < 0.5 • ( -> definition of matching jet) • The top candidate itself must fulfill: R(Rec. top, MC top) < 0.25 • ( -> reject badly reconstructed events) • The selected lepton must fulfill: R(Rec. lep., MC lep.) < 0.01 • ( -> the right lepton must have been found) • The permutation that fulfills all these requirements for 4 jets is declared as true jet pairing (black curves). All others are filled as wrong pairings (red curves). The normalized distributions are used as probability density functions (PDFs) 12/19

  14. Mass of 2-jet-permutations: • True combinations: Both jets from W • False combinations: All other permutations • (Right combinations of two jets • peak at W mass) • Angle between 2-jet-permutations: • True combinations: Both jets from W • False combinations: All other permutations • (The jets from a W tend to have a smaller • angle) JPM PDFs 13/19

  15. Angle sum of 3-jet-permutations: • True combinations: All jets from had. top • False combinations: All other permutations • (Right combinations of three jets tend to have a smaller angle sum due to boost) •  between top and anti top: • True combinations: 3 jets from top / • one jet/lepton from other top • False combinations: All other permutations • of 3 to 1 jet+lepton • (Right combinations tend to be antiparallel in ) JPM PDFs 14/19

  16. Angle between lepton and b jet: • True combinations: b jet lep. side / lepton • False combinations: All other permutations • (Right comb. of lepton and b jet tend to have a smaller angle due to boost) • pT sum of 2-jet-permutations: • True combinations: Both jets are b jets • False combinations: All other permutations • (The b jets tend to have a tiny higher • transverse momentum than other jets) JPM PDFs 15/19

  17. JPM (likelihood cut) • Final selection cut:Likelihood > 0.85 • This cut is mainly to have a good probability to choose the right three jets (from top) • This cut obviously also reduces much of the remaining background • After the final selection one gets an S/B of about 6 • For the following top mass plots only events with a LR of more than 0.85 are taken (207 semileptonic events remain). 16/19

  18. with in situ cal. (Not stacked) w/o in situ cal. Top signal 0.1/fb • Top signal clearly visible! • But high combinatorial background: • In about 50% the correct W was found • In about 35% the correct top was found • Problem: Higher purity needs higher cut on JPM likelihood, but too less statistics! 17/19

  19. Use net parameters at this point of training Outlook- Use ANN? • Artificial Neural Networks (ANN) uses correlations between input variables! But: Need three times more MC (training and validation) • First look at different network topologies (1/2/3 hidden layers and different number of perceptrons) using SNNS http://www-ra.informatik.uni-tuebingen.de/SNNS/ • Can an ANN improve the JPM (likelihood) efficiency? -> Studies ongoing… • As an example: • Training (black) and validation (red) of an ANN: • Two important issues: • 1.) For each net take configuration with minimum of validation error • 2.) Of all nets take the one with • the smallest validation error • (empirically search for best net) 18/19

  20. SIM Real: MTCC Summary • The top quark can clearly be identified with 0.1/fb of data (within the „1st physics run“ ) which can be collected in a couple of weeks (1032 cm-2s-1) without using any b tagging • Background can almost be eliminated using lepton isolation, jet pt, event shape variables like circularity and the JPM likelihood • A final S/B of about 6 was achieved with the use of a likelihood • Remaining problem so far: Combinatorical background is high (Can an ANN help?) • http://www.physik.rwthaachen.de/~cmsmgr/analysis/ 19/19

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