Tracker reconstruction in CMS for HLT and offline Teddy Todorov IReS, Strasbourg Helsinki B-  workshop 31 st May 2002

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 2 Track reconstruction strategy Track reconstruction is logically divided in four phases: Generation of track seeds Building of trajectories from seeds Resolution of ambiguities Final fit (smoothing) of track There are other ways to decompose the problem, and this decomposition does not work for some global methods (e.g. NN) But so far it work very well in CMS

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 3 Tracker geometry model The CMS tracker is a homogeneous collection of silicon detectors organized in layers. There are two types of layers: Barrel – cylinders Endcap – discs The track reconstruction is done mostly in terms of layers. Again, this is a specificity of the CMS tracker.

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 4 Seed generation The generation of seeds can be internal to the tracker, or external. Internal seeds are pairs of hits on seeding layers. External seeds involve other detectors (calorimeters, muon stations)

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 5 Internal seeding

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 6 Internal seeding… The seeding is “biased” by the interaction region and by the minimal Pt of the tracks –To some extent this is always the case The seeding can be restricted to a part of a layer (e.g. a jet cone), I.e. it can be regional.

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 7 Choice of seeding layers An obvious choice would be the outermost layers, since the occupancy is lowest there. But in CMS Between 8% and 15% of the 1 GeV pions interact before crossing 8 layers The outer layers don’t have stereo information The innermost layers are “pixel”, with very low channel occupancy and excellent 2D resolution Therefore the pixel layers are the favored seeding layers

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 8 Seeding combinatorics At high LHC luminosity and for QCD type of events the number of seeds compatible with the interaction region can be very large (tens or hundreds of thousands) A very efficient way to reduce this number is to find the primary vertex before starting track reconstruction (see the talk of Danek). It is possible to fully reconstruct all seeds within the offline time limits, but it’s not a very useful thing to do.

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 9 Trajectory building The next step is, starting from a seed, to reconstruct all possible trajectories. Technically this involves Finding the “next” layers to use (navigation) On those layers, finding the hits compatible with the predicted track state “updating” the track state with the hit information This naturally leads to a combinatorial explosion, so some logic is applied to keep the number of candidates “reasonable”

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 10 Worst case…

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 11 Resolution of ambiguities A single seed typically produces ether no tracks at all or several track candidates These candidates are “mutually exclusive” in the sense that they share many hits The ambiguity resolution is currently very simple, just based on the fraction of shared hits (the “best” candidate survives) Sometimes a single seed gives three valid tracks! (electron with a converted brem)

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 12 Final fit The trajectory building uses the Kalman formalism and results in a optimal forward fit (track parameters known at the outer end of the track) To obtain optimal parameters everywhere a Kalman smoothing is performed. Sometimes the seed generator biases the trajectory by a significant vertex constraint. To remove the bias the forward fit can be redone before smoothing.

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 13 HLT versus offline So far things are so general that the apply both to offline and to HLT tracking In fact, in CMS there is no distinction between HLT and offline software: Both use the same framework Algorithms can be freely moved from one to the other

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 14 HLT mind frame In High Level trigger reconstruction only 0.1% of the events should survive. So the main problem is “how can I kill this event using the least CPU time?” This can be interpreted as The fastest (most approximate) reconstruction The minimal amount of precise reconstruction A mixture of the two The problem is not the signal events that are kept, but the backgrounds that are rejected

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 15 So far we have chosen the second option The most precise treatment of hits (Kalman filter) is also the most efficient: it leads to smallest search windows, and to greatest rejection power of outlying hits. Therefore it leads to smallest combinatorics, and is the “fastest”! In the CMS tracker it is impossible to ignore multiple scattering and energy loss for tracks below about 10 GeV (which are most time consuming). So it’s difficult to use faster approximations.

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 16 High Level Trigger time scale Input rate: 100 kHz Output rate: 100 Hz Average CPU time per event: order of GHz processor What can the Tracker do at this level?

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 17 HLT Data volume constraint None! The current DAQ design provides fully assembled events in the builder units after Level1 All tracker Digis available The only constraint is CPU time

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 18 Partial reconstruction Basic idea: do the absolute minimum of reconstruction needed to answer a specific question Use the same reconstruction components as the full reconstruction –No need for writing, debugging, maintaining several tools for same task –No compromise on efficiency or accuracy except from limit on number of hits

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 19 Example: Tracker L2 muon trigger Conditions: –High Pt threshold – around 15 GeV –Primary muon: transverse impact parameter below 30 microns –Direction known from L1 with 0.5 rad accuracy Tracker information needed: confirm existence of track with the selection criteria above

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 20 Constraint from L1 trigger

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 21 Partial reconstruction Good resolution with only 5 hits [ Riccardo ]

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 22 Muon L2 with Tracker Tracker information needed –About 10 compatible Tracker pixel seeds at low luminosity –About 2.3 additional hits per seed need to be considered to reject it Using regional seeding and Pt cut in trajectory building, it takes about 30 ms to reject L1 muon candidate with ORCA5 Tracker can be used at Level 2!

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 23 B trigger algorithm Input: L1 jet PixelSelectiveSeeds PixelLines [ Danek ] Minitracks with pixel hits Primary Vertex from pixel ΔR around jet directions CombinatorialTrajectoryBuilder Stopping condition at n hits

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 24 Region of Interest Best Region of Interest ΔR<0.4 [ Livio ] Average number of tracks 100 GeV sample [PYTHIA/Lucell] ΔR cutAll Primary Secondary ΔR # of tracks (dijet events)

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 25 Efficiency bb jets Fake Rate below 1%[ Riccardo ] Track Efficiency (for b tracks) (5 hits) Fake Rate (5 hits) Jet info from Lucell E t =100 GeV ΔR<0.4

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 26 B-tag performance E t =100 GeV jets barrel 0.<|η|<0.7 Rejection factor u jets ~10 with b jets efficiency <80% (online) [ Gabriele ] Jet-tag: 2 tracks with S IP >0.5,1.,1.5,2.,2.5,3.,3.5,4. OFFLINE HLT

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 27 Sign flip of IP L1 jet (poor) resolution in η and φ (σ~0.1) [ Livio ] 2d transverse IP sign flip [ Gabriele ] η rec - η sim σ η ~0.1 u b OFFLINE – Lucell HLT-L1 Jets

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 28 Jet axis measurements [ Livio ] L1 jets η L2 jets η L1 jets + Tk η L1 jets φ L2 jets φ L1 jets + Tk φ σ η =0.112 σ η ~ ms CPU σ η ~ ms CPU σ φ =0.126 σ φ ~0.034 σ φ ~0.024

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 29 L1+Tracks B-tag E t =100 GeV jets barrel 0.<|η|<0.7 Online performance is better with L1+Tk jets!! [ Gabriele ] OFFLINE HLT Jet-tag: 2 tracks with S IP >0.5,1.,1.5,2.,2.5,3.,3.5,4.

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 30 L1+Tracks B-tag (2) E t =100 GeV jets barrel 0.<|η|<0.7 Better b jets efficiency with 3d IP [ Gabriele ] Jet-tag: 2 tracks with S IP >0.5,1.,1.5,2.,2.5,3.,3.5,4. OFFLINE HLT

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 31 Timing bb jets Increasing of reco time towards forward regions Tagging algorithm: <10 ms/ev !!! [ Riccardo ] E t =100 GeV no PileUp ΔR<0.4 5 hits maxCand=3 Jet info: Lucell

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 32 Timing measurements Pixel Readout: PixelReconstruction::doIt Seed Generator: PixelSelectiveSeeds::seeds[< 5%] Trajectory Builder: CombinatorialTrajectoryBuilder::trajectories[>80%] Trajectory Smoother: KalmanTrajectorySmoother::trajectories[<10%] Trajectory Cleaner: TrajectoryCleanerBySharedHits::clean[~ 1%] Trajectory Builder: CombinatorialTrajectoryBuilder [ModularKFReconstructor::reco] Tagging: BTaggingAlgorithmByTrackCounting::isB

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 33 Secondary Vertex [ Pascal ] CPU time rises as N 2 : –O(N 2 ) vertex fits, i.e. track propagations + matrix algebra 50 GeV barrel jets Can be improved by at least a factor 2 doing track linearization only once Whole event 1 CPU time (sec/1 GHz CPU) N tracks in both jet cones ε tag (%) RMS [ms]σ(t)[ms] bb61± uu1.0±

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 34 Tau case: Isolation Algorithms Signal vertex identified by: Pxl: leading track (P T >3GeV) Trk: best signal vertex candidate from pixel Reconstruction. signal vertex leading track jet axis jet matching cone  R = 0.1 signal cone reg Tk cone isolation cone Pxl: use pixel lines (i.e. tracks reconstructed only with pixel layers). Trk: use regional tracker reconstruction. Both algorithms count number of tracks inside signal (N SIG ) cone and isolation cone (N ISO ). Events is accepted if leading track exists and N SIG = N ISO

Helsinki b-  workshop 31 st May 2002 Track reconstruction in CMSTeddy Todorov 35 Conclusions Using the same track reconstruction framework and algorithms it is possible to achieve both offline requirements on reconstruction efficiency and accuracy and HLT requirements on CPU speed and rejection power