B Physics Lessons from the Tevatron Sinéad M. Farrington University of Oxford YETI Durham January 2008

Observation of Bs Mixing - In 2006 the phenomenon of mixing was observed for the first time in the Bs meson system This analysis required understanding almost every challenge we face in hadron collider B physics I will use it to illustrate the lessons we have learned (Note that there are many other B physics analyses at the Tevatron, but this one addresses nearly all of the issues) I will give more examples from CDF, reflecting my experience and affiliation 2

The Tevatron CDF D0 p 1km Ecom=2TeV - Ep=0.96TeV ECoM=2TeV - Fermilab, Chicago Currently the world’s highest energy collider Hadron collisions can produce a wide spectrum of b hadrons (in a challenging environment) Bs cannot be produced at the B factories since their Centre of Mass energy is below threshold (except for a special run by Belle) 3

Bs Physics Bs Bs Bs b s Bound states: Matterantimatter: b s Vts* Bound states: Matterantimatter: b s u, c, t, ? u,c,t,? b s Bs MATTER ANTIMATTER NEW PHYSICS? Vts* occurs via Bs Bs W+ W- Vts The mass eigenstates (H and L) are superpositions of Bs and Bs System characterised by 4 parameters: masses: mH, mL lifetimes: GH, GL (G=1/t) Difference in mass is related to frequency of oscillation between matter and antimatter particle Predicted Dms around 20ps-1

Measuring ms In principle: Measure asymmetry of number of matter and antimatter decays: In practice: Dm is measured by more complex techniques: amplitude scan and likelihood profile H. G. Moser, A. Roussarie, NIM A384 (1997) Measurement interesting because: New Physics may enter in box diagram Leads directly to measurement of CKM matrix element Vts 5

Mixing Ingredients Whichever technique is used, the information we need to extract from the events is: 1) Signal samples - semileptonic and hadronic modes Time of Decay Flavour tagging - was the B in a mixed state when it decayed? 6

Signal Samples for BsMixing Hadronic: fully reconstructed Semileptonic: partially reconstructed  L These modes are flavour specific: the charges tag the B at decay Need to gather large samples of these decays 7

Lesson 1: Triggering To trigger leptons: Bs → J/y f, Bs → Ds- l+ B mesons have long lifetimes (~ps) their decay products have large impact parameter, d0 d0 Secondary Vertex Primary Vertex Online accuracy Require two displaced tracks (CDF): (pT > 2 GeV/c, 120 m<|d0|<1mm) Need precision tracking in silicon vertex detector To trigger leptons: Bs → J/y f, Bs → Ds- l+ look for activity in muon chambers/ calorimetry Single- or di- lepton triggers D0 has superb muon coverage Trigger in |h| <1.6 (single) |h|<2 (di) CDF trigger in |h|<1 The experiments have focussed their analyses in complementary ways

Lesson 1: Triggering p = e-t’/  R(t’,t)  (t) Requiring displaced tracks biases the B lifetime (remember exponential distribution: the most likely value for the proper decay time is actually zero for B mesons) Correct for this bias using MC Calculate the “bias” as a function of the B’s proper decay time p = e-t’/  R(t’,t)  (t) intrinsic B lifetime resolution 0.0 0.2 0.4 proper time (cm) signal probability In addition we must confirm the trigger requirements so we take no volunteers

Lesson 1: Triggering Application at LHC: LHCb has displaced track triggers Made possible by the precision of the silicon detectors and the trigger hardware d0 resolution of 30mm for 2GeV track CMS/ATLAS cannot trigger on displaced tracks per se ATLAS uses Region of Interest triggering to collect some hadronic decays Mostly restrict B analyses to dimuon channels which are easier to trigger (excellent muon coverage so these results will be powerful in e.g. Bs→mm,Bs→mmf)

Lesson 2: Background Estimation Invariant mass distribution of Dsp: partially reconstructed B mesons Understand this contribution using MC plus measured branching fractions in constrained fit to data signal Bs→ Ds, Ds →   → K+K- Combinatorial Background Extrapolate function from high sideband B0→ D- decays

Background Estimation Example Simple case: Average the sidebands above and below D peak in semileptonic decay to obtain background estimate under the peak Harder case: partially reconstructed B mesons (missing photons/neutrals) populate lower sideband but not the signal region In this case can extrapolate from higher sideband into signal region Thus predict expected background events in signal region

Lesson 3: Neural Networks Tevatron is obviously not the first place to use multivariate techniques, but their power has been manifest Neural Network selection used in mixing analysis to increase signal yield Neural net can “learn” the characteristics of signal compared with background Exploit correlations among distinguishing variables More powerful than cuts based analysis LHC application Should not be in the first iteration analysis – requires understanding MC/data comparisons first Will be widely used at LHCb/CMS/ATLAS

Flavour Tagging To know whether a B meson “mixed” or not, need to know flavour (B or B) when it was produced flavour (B or B) when it decayed To determine B flavour at production, use tagging techniques: b quarks produced in pairs  only need to determine flavour of one of them fragmentation K p Bs Ds f p- Same Side K Tag Opposite side Same Side Opposite side tags understood well at B factories, LEP/SLD and applied at Tevatron Can make huge gains by using same side tagger 14

Lesson 4: Particle Identification This is the first time this type of tagger has been implemented Principle: charge of B and K correlated Use Time Of Flight detector, dE/dx information from tracking detector to select Kaon track The kaon is also selected based on its kinematics Bs s b u K+ } 15

Lesson 4: Particle Identification LHCb has excellent PID RICH: 16

Same Side Kaon Tagger CDF Public Note 8206 If MC reproduces distributions well for B0,B+, then rely on MC to predict tagger performance in Bs (with appropriate systematic errors) There is no good way to calibrate this tagger using the data. Enhance kaon fraction by making selection on particle ID tagging track It is then key to assign appropriate systematic errors 17

Lesson 5: use data and MC in tandem We could not have relied on the MC so heavily in this analysis if we could not compare distributions between data and MC Is this being fully addressed at LHC? In many analyses, the answer is yes (personal opinion) More focus needs to be placed on this approach Cannot assume that e.g. rates in MC will be accurate in data as we don’t understand, for example, how to tune the underlying event

Lesson 6: Fitter Amplitude scan performed on Bs candidates Inputs for each candidate: Mass Decay time Decay time resolution Tag decisions Predicted dilution Extra power to distinguish signal and background is obtained in the fitter by fitting to the mass simultaneously All elements are then folded into the amplitude scan “With three parameters, I can fit an elephant.” (Kelvin) 19

Combined Amplitude Scan Amplitude consistent with 1 probability of fake from random tags = 8x10-8 Equivalent to 5.4s significance ms = 17.77±0.10(stat)±0.07(syst) ps-1

(aside - Lesson 7: Branching Fractions) Measure relative branching fractions when possible To measure an absolute branching fraction have to know trigger efficiency, reconstruction efficiency, b species production fractions etc Relative branching fraction eliminates all of these concerns Choose a high statistics normalisation mode it should be taken with the same trigger ideally it should be a decay of the same B species, otherwise you have more work to do and you are penalised anyway by fs/fd uncertainties 21

Summary Wish list detector excellent impact parameter resolution excellent particle identification excellent mass resolution high trigger bandwidth and appropriate triggers muon/electron coverage LHC has the ideal detectors for furthering the B physics programme All it needs are… Wish list students/postdocs be detector aware – know its capabilities and get the calibration procedures in place fast trigger aware – figure out now which triggers to use, fight for your bandwidth! (quick?) fit fitters – get fitters ready early, add variables to get max power neural net aware – don’t use NN on day 1 data, but get them ready, get ready to make fast data/MC comparisons so you can move to NN background aware!!! – don’t assume you can use MC to figure out background, figure out now how you’re going to calculate the backgrounds

Outlook This measurement decreases uncertainty on CKM triangle apex: Easter 2006 October 2006 LHCb with 10fb-1

2) Time of Decay Measure pT of decay products Proper time resolution: Reconstruct decay length by vertexing Measure pT of decay products Proper time resolution: Semileptonic: Hadronic: osc. period at ms = 18 ps-1 Crucial: Vertex resolution (Silicon Vertex Detector, in particular Layer00 very close to beampipe) 24

Layer 00 So-called because we already had layer 0 when this device was designed! UK designed, built and (mostly) paid for this detector! I.P resolution without L00 layer of silicon placed directly on beryllium beam pipe Radius of 1.5 cm additional impact parameter resolution

Systematic Errors A reminder of what we’ve done: modified MC checked that it compares well with data for Bd, B+ compared efficiency and dilution for Bd, B+ from data and MC on the basis that they compare well, we extracted efficiency and dilution for Bs from MC To apply these numbers in our analysis we need a good understanding of the systematic errors Several sources: bb production mechanism fragmentation fraction particle content around the B variation within data statistics B** fraction particle ID detectors simulation pile-up will discuss further 26

Efficiency and Dilution in Data/MC After all the modifications we compare all relevant distributions including efficiency and dilution in data and MC for Bd, B+ B+ Bd (%) Eff Dil Data 58.4±0.5 25.4±1.4 57.2±0.6 14.2±2.9 MC 55.9 ±0.1 24.5±0.3 56.6±0.1 12.9±0.4 So we conclude that for Bd, B+ there is a good match between data and MC. Thus we use MC for the Bs case. 27

bb Production Mechanism Three main methods for producing bb in proton-antiproton collisions Flavour Creation, Flavour Excitation, Gluon Splitting (default mix 5:11:4) In gluon splitting case, the two b’s are close together – could pick up tagging tracks from the opposite b Systematic error obtained by fitting Df distribution in data and varying MC distribution within the errors This results in varying Gluon Splitting [-68%,+46%], Flavour Excitation and Creation [-50%,+50%] Q 28

Semileptonic Samples: Ds- l+ x Fully reconstructed Ds mesons: Bs mesons not fully reconstructed: Mixing fit range Particle ID used; new trigger paths added  The candidate’s m(lDs-) is included in the fit: discriminates against “physics backgrounds” of the type B0/+ → D+Ds 61500 semileptonic candidates

Calibrating Flavour Taggers e-/m- p+ ne/m b b K+ c Bd K- Opposite side D- Same Side p- Opposite side: can be calibrated with a sample of any B meson: Take sample of B+/Bd (mixing behaviour known) – “same side” Calculate how many B’s in that sample mixed Look on opposite side for leptons How often is there a lepton? (efficiency, e) How often is it the correct sign? (dilution, D) This is a completely data driven method to obtain the tagger power BUT! We can’t apply this to the same side tagger! 30

Comparisons for Bs already showed some comparisons for Bd and B+ here are comparisons for Bs Limited statistics  statistical error of comparison is included as systematic error 31

Multiple p-p interactions More than one pp pair can interact in a bunch crossing Gives rise to additional particles (usually low momentum) These could be additional SSKT tagging tracks Pythia does have a switch to simulate this but in this analysis data is used to simulate additional tracks 1)Calculate how many additional tracks should be added do this by looking at the (N+1)th event in our data files count how many tagging tracks are present in the signal region 2)Harvest tagging tracks from data (which fail one or more of the real cuts) 3)Embed these tracks in MC according to fraction determined in 1) 32

Fragmentation Function E. Ben-Haim et al. Phys. Lett. B580, 108 (2004) The Lund string fragmentation model is used throughout This has a “z” parameter to define the fraction of energy the B meson takes from the string Default z parameterisation is symmetric Lund function use tuning to LEP data specifically for B mesons 33

Why is ms interesting? Probe of New Physics from Dmd from Dmd/Dms Lower limit on Dms Probe of New Physics - may enter in box diagrams 2) Measure CKM matrix element: Dmd known accurately from B factories Vtd known to 15% Ratio Vtd/Vts Dmd/Dms related by constants:  (from lattice QCD) known to ~4% So: measure Dms gives Vts CKM Fit result: Dms: 18.3+6.5 (1s) ps-1 Standard Model Predicts rate of mixing, Dm=mH-mL, so Measure rate of mixing Vts (or hints of NEW physics) 34

Modifications to MC Each of these modifications is driven by previous measurements, and then associated systematic errors are assigned Data and MC are used in tandem (and consultation with theorists on what are “reasonable” variations to assess systematics) Multiple proton-antiproton interactions Fragmentation Model As well as usual GEANT detector simulation, specific response of particle ID systems treated especially for this analysis Trigger prescales After these modifications we make a comparison of all of the relevant variables between MC and data for all B meson types 35

Fragmentation Track multiplicity, transverse momentum of B meson and fragmentation tracks are sensitive to fragmentation function Tuning made at LEP should apply equally at Tevatron Variables compare well with data for Bd, B+ No reason to be suspicious of fragmentation used as a default, but systematic errors assigned to cover the possibility of problems Simultaneous fit to these distributions to determine allowed parameters of the symmetric Lund function data not sufficient to give a tight constraint reasonable variation can be obtained, beyond which comparison between MC and data is degraded The variations are used to assign systematic errors: 36

Particle Content around B Meson Particle species around the B meson gives insight on fragmentation Agrees well between data and MC for Bd, B+ Slight discrepancy for Bs: (20.2±1.4)% kaons in data, (23.6±0.2)% in MC Vary the kaon fraction in the data downwards in two ways: reweight all events with a kaon tagging track reweight only those events with prompt kaons from b-string 37

Variation within Data Statistics We claim the comparisons of data and MC are good but that is only valid within the errors on our data! MC samples varied within ranges allowed by statistical uncertainties 38

Compare again with data Including all of the systematic errors we can remake the comparisons of data and MC Note that the MC distributions envelope the data distributions 39

Final SSKT Results Final Tagger Power: eS2<D2>= (4.0+0.9-1.2) % (Notes: <D> is the average dilution over all quantities, in reality we bin dilution in momentum, particle ID variable to get extra power. S is a scale factor to account for any differences between data and MC in dependency of dilution on variables. ) MC simulations combined with measurements in data make this measurement possible 40

The Results 41

Bs Dilution and Efficiency Results Statistical errors only: Bs (%) Eff Dil Data 49.3±2.3 21.8±0.8 MC 52.1±0.3 - What about the systematic errors? 42