S. Donati University and INFN Pisa 9th Topical Seminar on Innovative Particle and Radiation Detectors, May , Siena, Italy The CDF Online Silicon Vertex Tracker

Overview of the CDF-II detector and trigger The Online Silicon Vertex Tracker : - Physics motivation - Working principle - Architecture - Track finding and fitting technique SVT performance in the early phase of CDF Run II SVT upgrades Conclusions Outline

Time Of Flight The CDF-II Detector and Trigger CAL COT MUON SVXCES XFT XCES MUON PRIM. XTRP SVT L2 CAL L1 CAL GLOBAL L1 MUON L1 TRACK GLOBAL LEVEL 2 TSI/CLK detector elements 2-D COT tracks 1 (XFT) Fast SVXII readout (10 5 channels) ~10  s SVT: 2D tracks in silicon (drops stereo info) SVT:  d = 35 mm (at 2 GeV/c) Parallel design (12 slices in phi) reflects SVXII design

Why do we need the Silicon Vertex Tracker ? Extract the huge Tevatron beauty/charm production (   = 50  b) from the 1,000 larger QCD background (  = 50 mb) at trigger level bb pp Primary Vertex Secondary Vertex d = impact parameter (~100  m) B Decay Length Lxy L xy  450  m P T (B)  5 GeV In the pre-SVT age CDF was limited to leptonic modes (B  J/  X, B  lDX, suffering from low BR and acceptance) Two Track Trigger Pt(trk) > 2 GeV IP(trk) > 100  m Fully hadronic modes: 2-body charmless B decays, B S mixing, Charm. Displaced Track + Lepton (e,  ) Pt(lepton) > 4 GeV (was 8 GeV) IP(trk) > 120  m Semileptonic modes: high statistics b-hadron lifetimes, b tagging, b mixing

SVT: Silicon Vertex Tracker (Chicago-Geneva-Pisa-Roma-Trieste) SVT receives: -COT tracks from Level 1 ( ,P t ) -Digitized pulse height in SVX strips and performs tracking in a two-stage process: 1. Pattern recogniton: Search “candidate” tracks resolution. 2. Track fitting: Associate full resolution hits to roads and fit 2-D track parameters (d, ,P t ) using a linearized algorithm. SVX II geometry: 12  -slices (30°each) “wedges” 6 modules in z (“semi-barrels”) Reflected in SVT architecture

The SVT Algorithm (Step I) Fast Pattern Recognition Road Hardware Implemented by AM chip (full custom - INFN Pisa) : - Receives the list of hit coordinates - Compares each hit with all the Candidate Roads in memory in parallel - Selects Roads with at least 1 hit in each SuperStrip - Outputs the list of found roads FAST: pattern rec. complete as soon as the last hit of the event is read roads for each 30 ° slice - ~250 micron SuperStrips - > 95% coverage for Pt >2 GeV Single Hit SuperStrip (bin) “XFT layer” Si Layer 1 Si Layer 2 Si Layer 3 Si Layer 4

The SVT Algorithm (Step II) Track Fitting When the track is confined to a road, fitting becomes easier P i = F i  X i + Q i ( P i = p t, , d        ) P 0i +  P i = F i  ( X 0i +  X i ) + Q i P 0i = F i  X 0i + Q i  P i = F i   X i then refer them to the ROAD boundary Road boundary TRACK P 0i X5X5 XFT layer X 05 SuperStrip 250  m the task is to compute simple scalar products F i and P 0 i coefficients are calculated in advance (using detector geometry) and stored in a RAM Linear expansion of parameters in hit positions X i SVX layer 4 X4X4 X 04 SVX layer 3 X3X3 X 03 SVX layer 2 X2X2 X 02 SVX layer 1 X1X1 X 01

The SVT Boards Hit Finder AM Sequencer AM Board Hit Buffer Track Fitter SVXII Data COT tracks from Level 1 Super Strip Matching Patterns Roads Roads + Corresponding Hits L2 CPU Tracks + Corresponding Hits

 Sinusoidal shape is the effect of beam displacement from origin of nominal coordinates d = X 0 ·sin (  ) - Y 0 ·cos (  ) SVX only (X 0,Y 0 ) d  28 Aug 2001 data,  2 <40 no Pt cut Can find the beam consistently in all wedges even using only SVX X 0 = cm Y 0 = cm track SVT Performance (I) d -  correlation

I.P. with respect to beam position (online) : Independent fit on each SVXII z -barrel (6) Can subtract beam offset online : Run October Online fit of X-Y Beam position  ~ 45  m

Highlights of B physics (hadronic channels) B 0 s  D - s  + golden channel to resolve B 0 s fast oscillations B 0 s  D - s  + Bh+h'-Bh+h'- B  h + h' - crucial to understand CP violation in the B sector (CDF-II competitive and complementary to B factories)

Highlights of B physics (semileptonic channels) B+l+D0XB+l+D0X 1400 Bs  l D s  l[  ] High statistics semileptonic B samples are excellent for calibration, B + /B 0 and Bs/B 0 lifetime measurements,tagging and B 0 and Bs mixing (for moderate x s )

Why is the SVT upgrade important ? 4/4 – 4/5 1.looser matching criteria 2.Ghost roads 3.5 layers  larger Patterns 27  sec Time (  s) 5/5 4/5 This road share all hits with the 5/5. It’s a ghost. More memory for thinner patterns 4 LVL2 buffers  dead time depends on: total LVL2 latency fluctuations Empty SS

First step: new Associative Memory System Use standard cell chips to perform AM chip function Increase from 128 pattern/chip  4k pattern/chip (thinner roads, less fits, faster system, can cope with increasing Tevatron luminosity, increase coverage to forward region, lower pt threshold)

SVT performs fast and accurate 2-D track reconstruction (is part of L2 trigger of CDF-II). Tracking is performed in two stages: - pattern recognition - high precision track fitting Taking good data since the beginning of CDF Run II, many analyses in the field of beauty/charm physics only possible thanks to this device Upgrade of the system in progress to cope with increasing Tevatron luminosity Conclusions

CAL COT MUON SVXCES XFT XCES MUON PRIM. XTRP SVT L2 CAL L1 CAL GLOBAL L1 MUON L1 TRACK GLOBAL LEVEL 2 TSI/CLK detector elements New for CDF run II at Level 1: 2-D COT tracks available (XFT) Fast SVXII readout (10 5 channels) : ~ 2.5  s Track reconstruction with “offline” resolution at Level 2 SVT 2-D tracks (drop SVXII stereo info) Pt > 2 GeV/c parallelized design : 12  -slices (30°each) which reflects SVX II geometry Fast ! ~10  s (50 KHz L1 accept rate)  d = 35  m (at 2 GeV/c) d, Pt,  CDF Trigger in run II

Accurate deadtime model (ModSim) 7% M. Schmidt 4/5 sept Ini_lum=44* /5 simulation Ini_lum=10* /4  4/5+SVTupgrade+L2upgr Simulation: Ini_lum ~ 20*10 30 Low lum, Run IIa L2, no COT prob. To be done again.

 d 2 =  B 2 +  res 2 Present (online)69 Correct relative wedge misalignment63 Correct for d and  non-linearity 57 Correct internal (detector layers) alignment55 Correct offline for beam z misalignment 48 GOAL : SVXII + COT offline tracks (1wedge)45 beam size i.p. resolution  d (  m ) Can be implemented soon Need to have beam aligned Understanding the width of d distribution

Because of the linear approximation done by Track Fitters, SVT measures : d SVT  d /cos (  )   SVT  tan (  ) Becomes important for large beam offset Can correct it making the online beam position routine fit a straight line on each wedge Effect of non-linearity :

Commissioning Run - Nov : SVT simulation with SVX hits + COT Offline From SVT TDR (’96) : SVT simulation using RunI data  ~ 45  m Expected SVT performance

 = 21  m  = 0.33 ·10 -4 cm   = 2 mrad  : SVT – COT Curvature : SVT - COT d : SVT - COT SVT Performance (II) correlation with offline tracks

=  B 2 · cos  Can extract  B from the correlation between impact parameters of track pairs If the beam spot is circular :  B = 40  m But at present the beam is tilt (beam spot is an ellipsis) :  short = 35  m  long = 42  m Intrinsic transverse beam width

L2 : N. SVT tracks > 2 | d | > 50  m  2 < 25 Pt > 2 GeV/c L1 prerequisite : 2 XFT tracks Trigger selection successfully implemented ! About 200 nb -1 of data collected with this trigger where we can start looking for B’s ! Cut Online on chi2, P t, d, N.tracks

SVT racks