ZEUS Tracking Tutorial Rainer Mankel ZEUS Weekly Meeting 6-Nov-2006.

ZEUS Tracking ZEUS Tracking TutorialTutorial

Rainer MankelRainer MankelZEUS Weekly MeetingZEUS Weekly Meeting

6-Nov-20066-Nov-2006

6-Nov-2006 R. Mankel, Tracking Tutorial 2

Who needs tracking…?


““Who needs tracking…?”Who needs tracking…?” Different kinds of analyses Different kinds of analyses

have have very different ideasvery different ideas as as to which information tracking to which information tracking should delivershould deliver some analyses only test whether some analyses only test whether

there is a there is a “good” primary vertex“good” primary vertex some analyses only need to some analyses only need to

know (roughly) the know (roughly) the primary primary vertex positionvertex position

a substantial set of analyses a substantial set of analyses explicitly reconstruct more explicitly reconstruct more complex complex final statesfinal states using using track track parametersparameters

HERA-II state-of-the-art analyses HERA-II state-of-the-art analyses use use lifetime lifetime signatures signatures precision tracking precision tracking ((m, not cm)m, not cm)

Level of tracking requirem

ents


Which detectors do

our tracking…?


The ZEUS Tracking System The ZEUS Tracking System for HERA-IIfor HERA-II

Central Tracking Central Tracking Detector (CTD)Detector (CTD)

Straw Tube Straw Tube Tracker (STT)Tracker (STT)

e p

Micro-Vertex Micro-Vertex Detector (MVD)Detector (MVD)


The Central Tracking Detector The Central Tracking Detector (CTD)(CTD)

Cylindrical drift Cylindrical drift chamberchamber

Nine superlayers Nine superlayers (five axial + 4 (five axial + 4 stereo) with eight stereo) with eight layers eachlayers each

drift cells tilted by drift cells tilted by 4545oo with respect to with respect to radial directionradial direction

official coordinate official coordinate resolution ~160 resolution ~160 mm


The Straw Tube The Straw Tube Tracker (STT)Tracker (STT)

2 superlayers of 2 superlayers of straw straw chamberschambers in the forward in the forward region (5region (5oo-25-25oo)) 12 layers per superlayer12 layers per superlayer oriented in oriented in four stereo viewsfour stereo views 7.5 mm straw diameter, Ar/CO7.5 mm straw diameter, Ar/CO22

During 2005, STT had to stay During 2005, STT had to stay off due to off due to insufficient coolinginsufficient cooling 2005 data have no STT 2005 data have no STT STT cooling has been upgraded, STT cooling has been upgraded,

2004 + 2006 data have STT2004 + 2006 data have STT


The Micro-Vertex Detector The Micro-Vertex Detector (MVD)(MVD)

The forward section:The forward section: 4 wheels4 wheels each composed of 2 each composed of 2

layers of 14 Si layers of 14 Si detectorsdetectors

in total 112 hybrids, in total 112 hybrids, 50k channels50k channels

The barrel section:The barrel section: 30 ladders30 ladders each composed of 5 each composed of 5

modules of 4 Si modules of 4 Si detectorsdetectors

in total 300 hybrids, in total 300 hybrids, >150k channels>150k channels

The rear section:The rear section: Cooling pipes and Cooling pipes and

manifoldsmanifolds Distribution of FE, Distribution of FE,

slow control and slow control and alignment cablesalignment cables


The Layout of the MVD The Layout of the MVD BarrelBarrel

Major part of azimuthal acceptance covered by three Major part of azimuthal acceptance covered by three cylinders of ladders (cylinders of ladders ( six measurements per track) six measurements per track)

Optimal use of available space between beam pipe & Optimal use of available space between beam pipe & CTDCTD

Mech

an

ical vie

w

Tra

ckin

g v

iew


How Does Track

Reconstruction Work?


The Track Reconstruction The Track Reconstruction ChainChain

Coordinate reconstruction

Track pattern recognition

Track fitting

Vertex finding

Vertex fitting

Higher level analysis


MVD Cluster FindingMVD Cluster Finding Cluster algorithm is one of the Cluster algorithm is one of the

crucial items determining crucial items determining tracking resolutiontracking resolution

Present reconstruction uses Present reconstruction uses centre-of-centre-of-gravity algorithmgravity algorithm obtained 25-35 obtained 25-35 m resolution for vertical m resolution for vertical

incidenceincidence Alternative algorithms are under studyAlternative algorithms are under study

head-tailhead-tail three-strip-algorithmthree-strip-algorithm eta algorithmeta algorithm


Track Finding (=Pattern Track Finding (=Pattern Recognition)Recognition)

ZEUS uses a ZEUS uses a combined track combined track pattern recognitionpattern recognition of MVD of MVD and CTDand CTD not merely an extension of CTD not merely an extension of CTD

tracks into the MVDtracks into the MVD improved efficiencyimproved efficiency

complex complex multi-pass proceduremulti-pass procedure Main challenge: “ganging” of Main challenge: “ganging” of

barrel MVD stripsbarrel MVD strips 50% of clusters are ghosts50% of clusters are ghosts

Presently being extended into Presently being extended into forward areaforward area

This combined MVD-CTD-STT This combined MVD-CTD-STT pattern recognition is a pattern recognition is a major major highlight of ZEUShighlight of ZEUS reconstructionreconstruction


Track Finding (cont’d)Track Finding (cont’d)

Example: seed creation in barrel Example: seed creation in barrel and forward MVDand forward MVD


The Track FitThe Track Fit direction of flight

direction of filter

production vertex

Using the Using the Kalman filter method with smootherKalman filter method with smoother to account to account for multiple scattering and ionization energy loss on MVD for multiple scattering and ionization energy loss on MVD part of trajectorypart of trajectory

Also performs rejection of Also performs rejection of outlier hitsoutlier hits purification of purification of tracktrack

Working on extension of Kalman filter into forward region Working on extension of Kalman filter into forward region (MVD+CTD+STT)(MVD+CTD+STT)


““Does the track fit influence Does the track fit influence the quality of my analysis?”the quality of my analysis?”

Yes!Yes! Enhancements in the track fit during the last year have Enhancements in the track fit during the last year have

improved improved the optimal the optimal momentum resolutionmomentum resolution from 1.2% from 1.2% 0.8%0.8%

Also the parameter error estimates are now correct within ~ 5 Also the parameter error estimates are now correct within ~ 5 - 20%- 20% important for important for significance plots significance plots MVD considerably MVD considerably

improves momentum improves momentum resolution at large resolution at large momentummomentum

Direct impact on Direct impact on mass mass resolutionsresolutions


““Does the track fit influence Does the track fit influence the quality of my analysis?” the quality of my analysis?” (cont’d)(cont’d)

And this And this pays off pays off directly! directly! New kffit New kffit improves improves mass mass resolutionresolution ofof KK00

SS: by : by factor of 1.3factor of 1.3

J/J/: by : by factor of 1.8factor of 1.8

Huge gain Huge gain on S/on S/BB


And the Future?And the Future? Traditionally, tracks are classified according to their Traditionally, tracks are classified according to their

outermost CTD superlayer (SL1…SL9)outermost CTD superlayer (SL1…SL9) The The typical analysistypical analysis discards tracks below discards tracks below

CTD SL3CTD SL3 In future, the combined forward tracking In future, the combined forward tracking

(CTD+BMVD +FMVD+STT)(CTD+BMVD +FMVD+STT) will open up will open up the range below the range below ~20~20oo

Considerable increase of acceptanceConsiderable increase of acceptance


Coming Soon: New Coming Soon: New Rigorous Rigorous Track Track FitFit

For Combined Forward Tracking For Combined Forward Tracking we need a we need a powerful track fitpowerful track fit to to fully exploit all detector fully exploit all detector informationinformation inhomogeneous magnetic fieldinhomogeneous magnetic field in in

forward regionforward region huge amounts of material (CTD end-huge amounts of material (CTD end-

plate)plate) combination of STT, CTD and MVD hitscombination of STT, CTD and MVD hits

This is the task of the This is the task of the Rigorous Rigorous Track Fit (RTF) Track Fit (RTF) state-of-the-art Kalman filter, adaptive state-of-the-art Kalman filter, adaptive

treatment of field map, STT+CTD+MVD treatment of field map, STT+CTD+MVD at hit level, rigorous treatment of at hit level, rigorous treatment of multiple scattering & energy loss, multiple scattering & energy loss, navigation scheme, C++navigation scheme, C++

To appear in new software releaseTo appear in new software release

Standard (2006a.1)

new: Rigorous track fit

SL 1 tracks!


I’m really confused about all these

different kinds of tracks…


Why Different Kinds of Why Different Kinds of Tracks?Tracks?

Mainly owed to commissioning history, but Mainly owed to commissioning history, but the the picture is clearing uppicture is clearing up

(Re-)fitted tracks= end product of the track = end product of the track

reconstruction chain reconstruction chain the tracks the tracks you should use wherever you should use wherever possiblepossible

“Regular” tracks= output of pattern recognition, with = output of pattern recognition, with

some level of (non-rigorous) fit some level of (non-rigorous) fit applied applied interim productinterim product of of tracking chaintracking chain

CTDonly tracks = tracks reconstructed from CTD hits = tracks reconstructed from CTD hits only only for for testing purposestesting purposes only only

“ZTT”


Pre-Vertex vs. Vertex Pre-Vertex vs. Vertex TracksTracks

Initially, tracks are reconstructed Initially, tracks are reconstructed independently independently pre-vertex tracks: ZTTRHL, …pre-vertex tracks: ZTTRHL, …

Then, the Then, the vertex reconstructionvertex reconstruction groups them into a primary vertex, groups them into a primary vertex, secondary verticessecondary vertices primary vertex tracks: ZTTRPRM, …primary vertex tracks: ZTTRPRM, … secondary vertex tracks: ZTTRSEC, …secondary vertex tracks: ZTTRSEC, … non-vertex tracks (ZTTRHL,…)non-vertex tracks (ZTTRHL,…)

Important: because of the magnetic Important: because of the magnetic field, a meaningful momentum vector field, a meaningful momentum vector can only be calculated for a track can only be calculated for a track whose origin is knownwhose origin is known it it does not make sensedoes not make sense trying to trying to

calculate invariant masses etc using calculate invariant masses etc using non-vertex tracksnon-vertex tracks

p

p


Should I care about vertexing

methods…?


Primary Vertex Primary Vertex ReconstructionReconstruction Until recently, standard Until recently, standard

method for primary vertex method for primary vertex finding/fitting has been finding/fitting has been “kfvertex”“kfvertex” based on Kalman filter techniquebased on Kalman filter technique good resolution, but good resolution, but limited limited

efficiencyefficiency. Also . Also very slowvery slow.. Topology of Topology of heavy flavor heavy flavor

eventsevents poses additional poses additional challenges to primary vertex challenges to primary vertex finderfinder long-lived particles long-lived particles outliers outliers

Needed a robust method…Needed a robust method…

Residual of primary vertex x position


Need Robust Method Need Robust Method for Primary Vertexing for Primary Vertexing EstimationEstimation

Outliers (in vertex case: Outliers (in vertex case: outlier tracks) outlier tracks) destroy destroy qualityquality of primary vertex of primary vertex positionposition

There is the acute danger of There is the acute danger of discarding “good” tracks & discarding “good” tracks & keeping “bad” trackskeeping “bad” tracks local but not global local but not global

optimumoptimum need a sophisticated fit need a sophisticated fit

procedureprocedure

Primary vertex before outlier rejection

After successful outlier rejection

After unsuccessful outlier rejection

Truth


The Deterministic The Deterministic Annealing Filter (DAF)* Annealing Filter (DAF)* for Vertexingfor Vertexing

Replace Replace hard hard 22 cuts cuts by a by a smooth temperature-smooth temperature-dependent weight function, dependent weight function, which is sharpened by which is sharpened by iteratively lowering the iteratively lowering the temperaturetemperature more more robust determinationrobust determination

of primary vertexof primary vertex after convergence, the after convergence, the

resulting weight could be resulting weight could be used for used for taggingtagging

At ZEUS, we start from At ZEUS, we start from the “regular” vertex and the “regular” vertex and “refine” it with the DAF“refine” it with the DAF

Weight:

* R. Frühwirth, A. Strandlie Comp.Phys.Comm. 120 (1999) 197


Performance of Vertex DAFPerformance of Vertex DAF The DAF itself The DAF itself

obtains a obtains a similar similar resolutionresolution as as kfvertexkfvertex

But its But its efficiency efficiency is higher:is higher: similar similar to “regular” to “regular” vertexing, also for vertexing, also for low multiplicitylow multiplicity

DAF combined DAF combined with beam with beam constraintconstraint (DAFbeam) gives (DAFbeam) gives the the best primary best primary vertex resolutionvertex resolution

Pattern Recognition “ refitted

Kalman Filter


““What should I do to get the best What should I do to get the best primary vertex efficiency & primary vertex efficiency & resolution?” resolution?”

Be sure to run Be sure to run vertex DAF with beam constraintvertex DAF with beam constraint!! Tricky: as a matter of principle, the beam spot is Tricky: as a matter of principle, the beam spot is

calculable only after the bulk reconstruction in calculable only after the bulk reconstruction in zephyr, and thus not available at reconstruction zephyr, and thus not available at reconstruction timetime

Therefore, DAFbeam Therefore, DAFbeam should be run at Orange levelshould be run at Orange level ORANGE-doDAFVtx ONORANGE-doDAFVtx ON DAFVTX-BeamCstr ONDAFVTX-BeamCstr ON

fortunately it is fortunately it is very fastvery fast (only several ms/evt) (only several ms/evt) Will only work if Will only work if beam spot GAFbeam spot GAF for this period is for this period is

available (be careful with MC)available (be careful with MC)


… and why so much fuss about the

beam spot?


The Beam Spot…The Beam Spot… is also a powerful constraint for is also a powerful constraint for impact parameter impact parameter

& decay length& decay length analysis analysis is practically is practically uncorrelateduncorrelated with the tracks in the with the tracks in the

actual eventactual event gives an gives an unbiased referenceunbiased reference e.g. for decay lengths e.g. for decay lengths while primary vertex may be biased by other long-lived while primary vertex may be biased by other long-lived

particlesparticles Downside: this helps only in the Downside: this helps only in the transverse planetransverse plane

so in the end, the final reference for heavy flavor so in the end, the final reference for heavy flavor tagging is probably a “reduced DAF primary vertex” …tagging is probably a “reduced DAF primary vertex” …

let’s take one step at a timelet’s take one step at a time


The Beam SpotThe Beam Spot By design, in HERA-II the By design, in HERA-II the

beamsbeams have Gaussian have Gaussian widths of ~110 widths of ~110 m m horizontally and ~30 horizontally and ~30 m m verticallyvertically

Powerful constraint for Powerful constraint for impact parameter & decay impact parameter & decay length analysislength analysis

But movement of the beam But movement of the beam spot must be measured spot must be measured very accuratelyvery accurately position can undergo position can undergo

sizable movementssizable movements (~100 (~100 m) even m) even within a fillwithin a fill

beam spot GAFsbeam spot GAFs50

m

50

m


Recently We Have Directly Recently We Have Directly Measured The Beam Spot Measured The Beam Spot Width Width

mBSPx 883~, mBSPy 2020~,

Done with Done with impact parameter correlationsimpact parameter correlations of track pairs of track pairs H1 have recently copied this method, get similar resultsH1 have recently copied this method, get similar results


What should I know about

alignment & all that …?


Alignment IssuesAlignment Issues Naturally, it takes experiments years to squeeze Naturally, it takes experiments years to squeeze

the the ultimate precisionultimate precision out of a (silicon) tracker out of a (silicon) tracker reason: alignment needs to be known at the ~10 reason: alignment needs to be known at the ~10 m scalem scale

Pre-installation surveys measured positions of Pre-installation surveys measured positions of MVD sensors within ladders & wheels well, but MVD sensors within ladders & wheels well, but knowledge for 3D arrangements is less preciseknowledge for 3D arrangements is less precise

During 2002-04, During 2002-04, cosmic runscosmic runs were basis of a first were basis of a first track-level alignmenttrack-level alignment

The best alignment accuracy to date has been The best alignment accuracy to date has been reached using reached using tracks from ep collisionstracks from ep collisions ““eplocal” alignmenteplocal” alignment


How Alignment Improves the How Alignment Improves the Impact Parameter ResolutionImpact Parameter Resolution

Experts are Experts are working hardworking hard to improve the to improve the alignment alignment even furthereven further


Subtracting the beam spot width, Subtracting the beam spot width, we can estimate our track-level we can estimate our track-level resolutionresolution

Resolution in data Resolution in data still somewhat wider still somewhat wider than MCthan MC remaining alignment remaining alignment

uncertainty?uncertainty? But clearly But clearly more more

than good enoughthan good enough for for first round of MVD-first round of MVD-based analyses…based analyses…

In some regions (e.g. In some regions (e.g. ~180~180oo) we have to ) we have to rely on rely on two-cylinder two-cylinder trackstracks which have which have worse resolutionworse resolution

Three-Cylinder tracks

(not yet based on “perfect” fits)


How the aligned MVD allows How the aligned MVD allows detecting heavy flavor detecting heavy flavor signaturessignatures

500 m

DIS event from 12-Mar-2005

Primary vertex

D+ vertex

+

+

K


DD++ K K-- + + ++ZEUS 2005 reprocessed with ep alignment.

ICHEP06 conference paper.


DD00 K K-- ++


Do all our data have the same

level of alignment?


Do all our data have the same level of alignment?

Not yet… !Not yet… ! The 2005 data have been reprocessed The 2005 data have been reprocessed this this

springspring with eplocal alignment with eplocal alignment presently our most precise data (132 pbpresently our most precise data (132 pb-1-1 with MVD, with MVD,

no STT)no STT) the best data for analyses using the best data for analyses using precision trackingprecision tracking!!

We expect We expect reprocessing of the 2004 datareprocessing of the 2004 data with with eplocal alignment to start in Decembereplocal alignment to start in December now even with now even with forward MVD alignmentforward MVD alignment & & combined combined

forward trackingforward tracking Reprocessing of 2006-07 data will follow laterReprocessing of 2006-07 data will follow later


How can I access track

information for my analysis?


How can I access track information for my analysis?

The primary output of track reconstruction The primary output of track reconstruction are the are the Adamo tablesAdamo tables, which can be accessed , which can be accessed within an Orange jobwithin an Orange job ZTVTXPRM (primary vertex position)ZTVTXPRM (primary vertex position) ZTTRPRM (primary tracks)ZTTRPRM (primary tracks) ZTVTXSEC (secondary vertices’ positions)ZTVTXSEC (secondary vertices’ positions) ZTTRSEC (secondary tracks)ZTTRSEC (secondary tracks) ZTTRHL (pre-vertex tracks)ZTTRHL (pre-vertex tracks)

These tables are connected by relationsThese tables are connected by relations


How can I access track information for my analysis? (cont’d)

Orange provides standard blocks with Orange provides standard blocks with tracking & vertex tracking & vertex informationinformation note: these are not responsibility of tracking groupnote: these are not responsibility of tracking group do not trust contents blindlydo not trust contents blindly

Main danger: depending on control cards, Orange will fill Main danger: depending on control cards, Orange will fill tracks in several variantstracks in several variants danger of danger of double or triple countingdouble or triple counting be careful be careful

Recommendation: use Recommendation: use fitted tracks (ZTT)fitted tracks (ZTT) wherever wherever possible. You may have to watch for possible. You may have to watch for detailed cardsdetailed cards in in settings of individual blocks.settings of individual blocks.

ORANGE-TRACKING ZTT

[...]

C Tracking code for Charm finding parameters: SEE orange_Dmesons.fpp

ORANGE-CHARMTRK ZTT

[...]


Analysis-Level VertexingAnalysis-Level Vertexing Seen with MVD precision, Seen with MVD precision, standard vertex standard vertex

reconstructionreconstruction is not enough to detect all possible is not enough to detect all possible topologies automatically topologies automatically

For this reason, in ZEUS many precision-tracking For this reason, in ZEUS many precision-tracking vertex signatures are found by vertex signatures are found by context-dependentcontext-dependent revertexing techniques at analysis-levelrevertexing techniques at analysis-level based on tools in based on tools in tLite librarytLite library very powerfulvery powerful would hardly have been possible ~10 years ago (CPU would hardly have been possible ~10 years ago (CPU

time)time) example: revertexing Dexample: revertexing D++, D, D00, D*, V0Lite finders, D*, V0Lite finders


Analysis-Level Vertexing Analysis-Level Vertexing (cont’d)(cont’d)

Various Various revertexing particle findersrevertexing particle finders can used in can used in Orange just by toggling a cardOrange just by toggling a card revertexing Drevertexing D++, D, D00, D*, V0Lite & inclusive sec, D*, V0Lite & inclusive secdrydry vertex vertex

findersfinders

XY reconstructedXY reconstructed XY MC true + jetsXY MC true + jets

Revertexing charm Revertexing charm finders use their finders use their own “charm own “charm tracking block”tracking block”

Recently, analysis Recently, analysis vertices are even vertices are even displayed in ZeVisdisplayed in ZeVis


Can I develop my own vertex

analysis ?


Can I develop my own vertex analysis ?

Yes! the Yes! the tLitetLite library library holds many useful holds many useful tools:tools: fast vertex fitting at analysis levelfast vertex fitting at analysis level DCA and impact parameter calculationDCA and impact parameter calculation helix utilitieshelix utilities kinematic fitskinematic fits

Build your own Build your own sophisticated vertex sophisticated vertex cascadecascade analysis analysis enjoy…enjoy… and submit it to Orange when doneand submit it to Orange when done


Just an Just an Appetizer…Appetizer…

Analysis of Analysis of more complex final more complex final statesstates comes into reach comes into reach vertex and mass constraints on vertex and mass constraints on

whole decay chain will whole decay chain will increasingly play a roleincreasingly play a role

Example: Example: (2S) (2S) J/ J/ ++--

( (++--) ) ++--

simple mass calculation: only simple mass calculation: only weak signalweak signal

full kinematical fitfull kinematical fit of decay of decay chain: sharp signalchain: sharp signal

(2S)(2S)

????

simple calculation of invariant mass

full kinematical fit of decay chain


Need further information?Need further information?

ZEUS Tracking Web:ZEUS Tracking Web: http://www-zeus-data.desy.de/tracking/http://www-zeus-data.desy.de/tracking/


SummarySummary

This is an exciting time for tracking This is an exciting time for tracking in ZEUSin ZEUS

We have plenty of new data, and a We have plenty of new data, and a new level of precision & scope in new level of precision & scope in trackingtracking

There is a nice harvest ahead There is a nice harvest ahead it it is worthwhile to learn how to use itis worthwhile to learn how to use it


Additional Additional MaterialMaterial


The MVD BarrelThe MVD Barrel Single-sided n-doped Single-sided n-doped

silicon, 300 silicon, 300 m thick, pm thick, p++ strip implants, 20 strip implants, 20 m m pitchpitch

Readout pitch 120 Readout pitch 120 m m (capacitive coupling)(capacitive coupling)

RR and Z sensors are and Z sensors are gangedganged

Helix3.0 analog R/O chip Helix3.0 analog R/O chip (Heidelberg/NIKHEF) (Heidelberg/NIKHEF)

Five modules are Five modules are mounted on a mounted on a carbon fiber carbon fiber support structure support structure to form a ladderto form a ladder

Si planes, hybrids Si planes, hybrids and cabling are and cabling are located on the 3 located on the 3 planes of the planes of the ladderladder

30 ladders 30 ladders arranged in arranged in three cylinders three cylinders around elliptical around elliptical beam pipebeam pipe


The MVD Forward WheelsThe MVD Forward Wheels

The four forward wheels The four forward wheels have trapezoidal shape have trapezoidal shape detectors with two detectors with two different sizes to different sizes to accommodate the accommodate the beam pipebeam pipe

Each two layers of Each two layers of single sided detectors, single sided detectors, same pitch and same pitch and construction as in barrelconstruction as in barrel

strips cross at angle of strips cross at angle of 2626oo

Same electronics and Same electronics and connectivity as in barrelconnectivity as in barrel


I.P. ResolutionI.P. Resolution

~55 microns at pT~3.7~55 microns at pT~3.7 Strictly, this is for I.P. wrt (0,0)Strictly, this is for I.P. wrt (0,0)


2-Cyl

Two- and Three-Cylinder tracks


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ZEUS Tracking Tutorial Rainer Mankel ZEUS Weekly Meeting 6-Nov-2006.

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