1. Paper on J/  and b production Wenbin Qian, Patrick Robbe for the F-WG, Tsinghua Beijing/LAL Orsay, 2 Dec 2009

2. 2 Content « Measurement of the prompt J/ψ and b → J/ψ production cross-sections at LHCb » based on 5 pb –1 of data collected with muon and di-muon trigger use VELO and long tracking only + muon ID measure J/ψ → μμ differential cross sections:  4 bins in pseudorapidity η, between 3 and 5 in steps of 0.5  7 bins in p T, between 0 and 7 GeV/c in steps of 1 GeV/c  2 bins for overlap with ATLAS/CMS: 2 7 GeV/c in each phase-space bin:  extract prompt J/ψ and b→ J/ψ signal yields with combined fit of J/ψ mass and J/ψ “time” distributions  get efficiency from MC as a function of polarization (treat unknown polarization as systematics)‏ convert results to production cross-sections using luminosity estimate quote σ(B hadron) in LHCb’s acceptance

3. 3 Motivations (1)‏ J/  production is a process with large cross-section: large dataset available very soon after start of LHCb running. Use J/  sample to measure prompt and bb cross-section from the 2 processes: pp → X + (  (2S),  c0,1,2, … → X) J/  pp → X + bb (b/b → J/  X)‏ These measurements are important for later analysis steps in LHCb: Open the road to B physics with J/  and dimuon modes, Tune B hadron spectra in Monte-Carlo, Necessary input for absolute branching fraction measurements.

4. 4 Motivations (2)‏ Measurement itself is of interest: J/  production properties not well understood: NRQCD was successful in reproducing the p t spectrum measured at Tevatron, but not the production polarization. The measurement of the differential cross-section at higher energies is an important observable to understand charmonium production mechanism. Special coverage of LHCb experiment, where theoretical predictions are less accurate. Need O(10)% error in different p t and  bins to test predictions based on FONLL computations (uncertainties between 20% and 50%)‏

5. 5 Some numbers DC06:  (prompt J/  14TeV) = 0.266 mb ± 0.002 mb,  (bb) = 0.698 mb ± 0.001 mb MC09: same prompt J/  but higher bb (~0.900 mb)‏ Br(b → J/  ) = 1.16% ± 0.10% (LEP, 1992-1994), Br(b → J/  in Monte Carlo) = 1.46% ie  (b → J/  ) = 0.0204 mb ± 0.0008 mb in DC06 DC06 numbers used for this analysis

6. 6  as a function of CM energy Cross section (mb)‏ Cross-sections at 7 TeV are half of cross- sections at 14 TeV J/  Cross-section (mb)‏ bb Cross-section (mb)‏ Beam energy

7. 7 Generator Level Cross-section DC06 generator level differential cross-section for J/  with 3<  <5. This is what we want to measure and to re-inject in the Monte-Carlo.

8. 8 J/  Selection  +,  - : Long tracks, with  2 /n DOF <2 p t >0.7 GeV/c Loose Muon PID for both tracks (StdLooseMuon,  ln(L  )>-1)‏ J/  vertex:  2 /n DOF < 6 J/  mass window: ± 400 MeV/c 2 At least one primary vertex In case of several candidates per event, take the one with smallest  2 (  + )+  2 (  - ).

9. 9 J/  Selection Illustration on L0 minimum bias DC06 sample Mass resolution: 11.2 ± 0.4 MeV/c 2 S/B=17.6 ± 2.3 1.3x10 9 reconstructed after L0 J/  for 1 fb -1 (14 TeV)‏ Or 3.3x10 6 for 5 pb -1 at 7 TeV

10. 10 J/  Selection (MC 09, 7 TeV)‏ With L=10 28 cm -2.s -1 : 0.01 Hz With L=10 30 cm -2.s -1 : 1 Hz: Requires Trigger. Thomas Ruf

11. 11 J/  Stripping m(  )>2.7 GeV/c2 p T (  )>0.5 GeV/c  : long track, StdLooseMuons,  2 /n DOF <3 Vertex:  2 /n DOF <20

12. 12 Acceptance Efficiencies Between 20% and 98% (error 1%)‏ Identical for prompt and J/  from B

13. 13 Reconstruction Efficiency Between 8% and 63% (error 3%)‏ Identical for prompt and J/  from B

14. 14 L0 Trigger Efficiency Between 58% and 100% (error 1%)‏

15. 15 Distinguish prompt J/  from J/  from b Use: ++ Primary vertex z -- dz Simple approximation of « b quark » lifetime: t distribution at generator level

16. 16 How to measure differential cross- section Divide in J/  p t and  (pseudo-rapidity) bins Fit mass distribution to measure number of reconstructed J/  Use t distribution to measure number of reconstructed prompt J/  and J/  from b Use efficiency per p t and  bin to compute original number of J/  Bins definition: 0  p t  7 GeV/c, 7 bins 3  5, 4 bins

17. 17 Mass distribution Signal fitted with Crystal-Ball function: First order polynomial function for background

18. 18 t distribution t distribution has 4 components: Prompt J/  : peak at 0 ps J/  from B: exponential decay J/  background Long tail, due to association between J/  and wrong primary vertex. Signal Only

19. 19 t background distribution Background time distribution is determined from the data using events in the upper sideband of the J/  mass distribution: Upper-sideband of mass distribution on L0 Minimum Bias sample

20. 20 t signal distribution Function: R(t) is the resolution function obtained from data, using the negative tail of the prompt component: single Gaussian Prompt J/  J/  from b tail

21. 21 t Tail Distribution Long tail because of association of the J/  to a wrong PV when computing t: 20%: wrong choice of PV amongst the reconstructed ones. 80%: the correct PV is not reconstructed:  Because the number of reconstructed tracks from the PV is too small.  Because 2 PV are close to each other: this lead to a distribution close to t=0, and will be included in the prompt part. In total 1.7 % of the J/  are in the tail. Small effect, but not negligible compared to J/  from B: in the region 2 < t < 15 ps, 10.2% of the J/  are tail events.

22. 22 Tail distribution from data Method has been developped to obtain the tail shape from data. The PV of the « next event » in the J/  sample is used to simulate the position of an un-correlated PV: BUT: Yuanning Gao, Wenbin Qian Asymmetry is not reproduced correctly t(ps)‏ Tail Next event

23. 23 Why Asymmetry ? PV reconstruction efficiency (1-  ) depends slightly on z of PV:  = MC (computation #1)‏ Modified next event method

24. 24  (z) from data For a given z(J/  ): A(z) = Number of events with t z 10 ps (ie only tail events)‏ B(z) = Number of events with t z (next event) 10 ps.  (z)=A(z)/B(z)‏

25. 25 Tail estimation from data Then  Part at t~0 ps included in the prompt description

26. 26 Expected number of events per bins Combined mass-t fit gives number of reconstructed J/  events (prompt and from B) in each bin. Procedure exercised on inclusive J/  events + toy MC background reproducing background behaviour observed on Minimum Bias. Rescaled for 5pb -1 and 7 TeV: 3 - 3.53.5 - 44 - 4.54.5 - 5 0 – 1 GeV/c 1 – 2 GeV/c 2 – 3 GeV/c 3 – 4 GeV/c 4 – 5 GeV/c 5 – 6 GeV/c 6 – 7 GeV/c 4215 ± 73 143616 ± 414 122788 ± 386 38629 ± 226 12706 ± 128 4977 ± 82 2142 ± 55 41987 ± 222 232190 ± 505 134701 ± 400 36194 ± 210 10513 ± 117 3988 ± 69 1634 ± 44 90818 ± 325 236633 ± 524 107178 ± 352 24196 ± 172 6506 ± 87 2155 ± 52 799 ± 36 112309 ± 364 178834 ± 446 53316 ± 244 7236 ± 92 1315 ± 52 452 ± 37 98 ± 16 Prompt

27. 27 Expected number of events per bins Extra Bins 2 7 GeV/c may be interesting because it overlaps with ATLAS/CMS acceptance. 3 - 3.53.5 - 44 - 4.54.5 - 5 0 – 1 GeV/c 1 – 2 GeV/c 2 – 3 GeV/c 3 – 4 GeV/c 4 – 5 GeV/c 5 – 6 GeV/c 6 – 7 GeV/c 253 ± 21 6920 ± 101 10245 ± 119 8643 ± 123 7012 ± 91 4866 ± 75 3214 ± 60 1864 ± 53 11878 ± 128 10858 ± 121 7590 ± 96 5208 ± 78 3659 ± 64 2212 ± 50 3927 ± 76 11414 ± 128 8156 ± 105 4516 ± 76 2705 ± 57 1561 ± 43 1093 ± 36 5126 ± 87 7973 ± 105 3640 ± 69 1160 ± 39 559 ± 28 199 ± 18 126 ± 12 From B At least 100 events per bin

28. 28 Fit result: number of events per p t bin Corresponds to 0.145 pb -1, 14 TeV Signal: part of the inclusive J/  sample Background: toy Monte-Carlo reproducing behaviour seen on Minimum Bias sample. Number of prompt J/  Fraction of J/  from b Fit Monte-Carlo input values

29. 29 Cross-section determination From the number of reconstructed events, to obtain the cross-section: Need the integrated luminosity. Need the acceptance efficiency: geometrical effects only, use Monte-Carlo. Need the trigger, reconstruction and the selection efficiencies. Monte-Carlo efficiencies are used for the moment, correct them to the real efficiencies with data. Assume efficiencies independant of t (which is true in Monte-Carlo), only p t and  dependance.

30. 30 Fit result on MC sample Input value:

31. 31 Polarization - definitions  =1  =-1 In the helicity frame, cos  follows the distribution (integrating over  ):

32. 32 Polarization LHCb geometry induces fake J/  polarization When we generate unpolarized J/  cos 

33. 33 Polarization Problem: production polarization of J/  is unknown and very likely different from 0 (we have 0 in the DC06 and MC09 Monte Carlo)‏ Tevatron measurements show disagreement with theoretical predictions. This effect will be assigned to a systematic error on the cross-section measurement for the first paper, but a measurement of the polarization (which is one of the most interesting number for prompt J/  ) will be done later (in several frames, taking into account azimuthal distributions). Weight full Monte-Carlo sample and compute the efficiencies for 3 different assumptions:  =0,  =-1 and  =+1. Then take difference as systematic error, per p T and  bin.

34. 34 Polarization Systematics Between 1% and 44% bias

35. 35 Systematics Statistical error ~10 % per bin Error on tracking, muon PID, vertexing: See Thomas talk Error on luminosity: See Vladik's talk Error on Br(b → J/  X): 9%, affects b cross- section measurement Polarization: between 1% and 40%. Fit errors: incomplete description of mass and t shapes (radiative tail,...): 2%.

36. 36 Conclusions Selection and fit procedures established for the measurement of prompt J/  and bb cross- sections. Part of the distributions can be determined from data directly. Large effect due to unknown polarization. Note written by Wenbin is circulating in F-WG. Skeleton of article in preparation. A lot of work to do to be able to understand/use first data: see Thomas talk.