A tool for classifying ghost tracks together with the results obtained with Brunel v31r8 is described

Needham, M

CERN Document Server

LHCb note 2007-128Classification of Ghost TracksM. NeedhamCERN ∗October 5, 2007AbstractA tool for classifying ghost tracks together with the results ob-tained with Brunel v31r8 is described.∗Present Addresss: Laboratoire de Physique des Hautes Energies, Ecole PolytechniqueFe´de´rale de Lausanne1 IntroductionThe efficiency of the majority of the LHCb tracking algorithms is limitedby the ghost rate in the detector that can be tolerated. This is particularytrue for luminosities greater than the nominal LHCb running value of 2 ×1032 cm−2s−1. In this note a tool that classifies ghost tracks is presented.This tool allows the origin of ghost tracks to be better understood. Thus,it is hoped thus better strategies for identifying and removing ghosts can bedeveloped.This note is structured as follows. First, the proposed classification schemeand its implementation is discussed. This is followed by a discussion of theresults found running the tool on Brunel v31r8.2 Classification SchemeBased on studies of the origin of ghost tracks the following categorizationscheme is proposed. Nota Bene, the scheme proposed is not unique: sometracks can be placed in more than one of the categories. Therefore, thecategories have been ranked according to how informative they are. Eachtrack is checked against each category in turn until the first match is found.The categories are:Spillover and noise: 70% of the LHCbIDs assigned to the track are notrelated to any MCParticle 1.Decay in flight: 70% of the LHCbIDs assigned to the track are due to akaon or pion and its daughter muon.Conversion: 70% of the LHCbIDs assigned to the track are due to anelectron pair produced by a photon conversion within the detector.EM: 70% of the LHCbIDs assigned to the track are due to an electron.Hadronic interaction: Two or more of theMCParticle that give LHCbIDsassigned to a track originate in hadronic interactions in the detector.1Clusters from both spillover and noise have no associated MCParticle. In fact thenoise rates simulated in the tracking detectors are low. Hence, all the tracks in the categoryare due to spillover.2Phi decay products: 70% of the LHCbIDs assigned to the track are dueto a K± pair produced in a decay of a φ.Ghost Parent: One or more of the ancestors tracks [1] used to constructthe track is a ghost.After this generic procedure has been run algorithm specific categorizationsare considered:Inconsistant parts Many of the LHCb tracking algorithms build tracks inindependent projections before combining these to give space tracks. Itcan be that the LHCbIDs corresponding to each projection are consis-tant but that two projections are not related to the sameMCParticle.In the case of the VELO tracking this corresponds to the hits in ther and φ projections being from different MCParticles. In the case ofthe T seeding this corresponds to the hits in the x and stereo layersbeing from different MCParticles. This category is also used to flaga ghost in the produced by the track matching and the forward track-ing where the T and VELO seed tracks are not related to the sameMCParticle.Combinatoric In the VELO r tracking combinations of three co-linear rhits are considered valid track candidates. If the three hits assigned tothe track are from unrelated MCParticles then this is categorized ascombinatoric.Finally, the tool can return two trivial cases: if no classification was possibleor if the tool has been called for a real track.3 ImplementationThe categorization scheme described above has been implemented as a Gauditool with the interface given below:1 stat ic const In t e r f a c e ID2 I ID ITrackGhos tC l a s s i f i c a t i on ( ” ITrackGhos tC l a s s i f i c a t i on ” , 0 , 0 ) ;34 class IT ra ckGhos tC l a s s i f i c a t i on : virtual public IAlgTool {56 public :78 typedef std : : vec to r<LHCb : : LHCbID> LHCbIDs ;39 /// Retr i eve i n t e r f a c e ID10 stat ic const In t e r f a c e ID&11 i n t e r f a c e ID ( ) { return I ID ITrackGhos tC l a s s i f i c a t i on ; }1213 /∗∗14 ∗ In format ion on what a ghost t rack i s . . . .15 ∗ @param LHCb : : Track to l i n k16 ∗ @param LHCb : : GhostInfo f i l l e d with ghost i n f o17 ∗ @return StatusCode18 ∗/19 virtual void StatusCode i n f o ( const LHCb : : Track& aTrack ,20 LHCb : : GhostTrackInfo& t i n f o ) const= 0;2122 /∗∗23 ∗ In format ion on a l i s t o f LHCbIDs24 ∗ @param LHCbIDs : : c o n s t i t e r a t o r s t a r t25 ∗ @param LHCbIDs : : c o n s t i t e r a t o r stop26 ∗ @param LHCb : : GhostInfo f i l l e d with ghost i n f o27 ∗ @return StatusCode28 ∗/29 virtual void StatusCode i n f o (LHCbIDs : : c o n s t i t e r a t o r & s t a r t ,30 LHCbIDs : : c o n s t i t e r a t o r & stop ,31 LHCb : : GhostTrackInfo& t i n f o ) const= 0;32 } ;The tool has two methods that can be called. The first takes a LHCb::Trackas an input. The second takes iterators that define a range of LHCbIDs. Thisallows the tool to be used in cases — such as the pattern recognition whereno LHCb::Track object has yet been built. In both cases the result ofthe categorization is filled into a LHCb::GhostTrackInfo object 2. Thiscontains both the result of the categorization procedure and also a mapcontaining the raw information on which MCParticles were linked to thetrack.Fig. 1 shows the class diagram for tools inheriting from this interface. Aconcrete base class (GhostTrackClassificationBase) provides the genericpart of the categorization procedure together with member functions for ma-nipulation of the raw MCParticle map. If only the generic categorizationis required this base class can be called directly. On the otherhand if an al-gorithm specific classification is need a specialized implementation inheritingfrom the base class can be made. This has been provided for the current2-D, Velo 3-D, T-seeding and long tracking algorithms.The following code fragments illustrate how to use the tool:1 #include ‘ ‘ MCInterfaces / IGhos tTrackC la s s i f i c a t i on . h ’ ’2 #include ‘ ‘ GaudiKernel / toStream . h ’ ’ // turns enums to s t r i n g s34 // get the t o o l5 IGho s tTrackC la s s i f i c a t i on ∗ gTool =6 t o o l<IGho s tTrackC la s s i f i c a t i on > ( ‘ ‘ LongGhos tC la s s i f i c a t i on ’ ’ ) ;2The header file is located in the MCEvent package.4IAlgToolIGhostTrackClassification GaudiToolGhostTrackClassificationBaseAlgToolVeloRGhostClassification VeloGhostClassification TTrackGhostClassification LongGhostClassificationFigure 1: Class diagram for the ghost track classification tool.78 // c a l l i t9 LHCb : : Track ∗ aTrack ;10 LHCb : : GhostTrackInfo t I n f o ;11 StatusCode sc = gTool−>i n f o ( aTrack , t I n f o ) ;1213 // get the c l a s s i f i c a t i o n14 std : : cout << ‘ ‘ C l a s s i f i e d as ’ ’15 << Gaudi : : U t i l s : : t oS t r i ng ( t i n f o . c l a s s i f i c a t i o n ()) << std : : endl ;4 Resultshe performance of the algorithm has been studied using 13000 Bd → J/ψ(µ+µ−)KS(pi+pi−)events generated for the DC’ 06 production and reconstructed with Brunelv31r8. Fig 2-7 show the results of running the algorithm for the Velo 2-D,Velo 3-D, Tsa seeding, Forward Tracking, Track Matching and Dowstreamtracking. It can be seen that:• For all algorithms there is a sizeable fraction of ghost tracks that aredue to electromagnetic interactions of photons and electrons within thedetector.• For the Velo 2-D tracking it can be seen that the largest source ofghosts is tracks with three hits that originate from different sources(combinatoric).• The only algorithm where the contribution from spillover is sizeable isthe Tsa seeding. This was known from previous studies [2].5• In the case of the ’long’ tracking algorithms the biggest source of ghostsis random matches of the T and Velo parts• More work is needed to improve the classification scheme. In particularfor the Velo 3-D and Tsa seeding the number of tracks which are notclassified is high.NoneEMConversionHadronicCombinatoricSpilloverDecayInFlightlllllll0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7Figure 2: R dotchart [3] showing the composition of ghost tracks in % forthe Velo 2-D tracking [4].References[1] J. Hernando Morata and E. Rodrigues. Tracking event model. LHCb-note2007-007.[2] R. Forty and M. Needham. Updated Performance of the T seeding.LHCb-note 2007-023.[3] R Development Core Team. R: A Language and Environment for Statisti-cal Computing. R Foundation for Statistical Computing, Vienna, Austria,2005. ISBN 3-900051-07-0.6InconsistantPartsEMConversionHadronicNoneDecayInFlightSpilloverAndNoiseFromPhiGhostParentlllllllll0.0 0.1 0.2 0.3Figure 3: R dotchart [3] showing the composition of ghost tracks in % forthe Velo 3-D tracking [4].InconsistantPartsEMConversionHadronicNoneDecayInFlightSpilloverAndNoiseFromPhillllllll0.0 0.1 0.2 0.3 0.4Figure 4: R dotchart [3] showing the composition of ghost tracks in % forthe Tsa seeding [2].7InconsistantPartsEMConversionHadronicGhostParentNoneDecayInFlightSpilloverAndNoiseFromPhilllllllll0 10 20 30 40 50Figure 5: R dotchart [3] showing the composition of ghost tracks in % forthe forward tracking [5].InconsistantPartsEMConversionHadronicGhostParentNoneDecayInFlightSpilloverAndNoiseFromPhilllllllll0 10 20 30 40 50Figure 6: R dotchart [3] showing the composition of ghost tracks in % forthe track matching [6].8NoneEMConversionHadronicGhostParentSpilloverDecayInFlightlllllll0.1 0.2 0.3 0.4 0.5Figure 7: R dotchart [3] showing the composition of ghost tracks in % forthe downstream tracking [7].[4] D. Hutchcroft et al. VELO Pattern Recognition. LHCb-note 2007-013.[5] O. Callot and S. Menzemer. Performance of the forward tracking. LHCb-note 2007-015.[6] M. Needham. Performance of the Track Matching. LHCb-note 2007-129.[7] O. Callot. Downstream pattern recognition. LHCb-Note 2007-026.9

Classification of Ghost Tracks

Abstract

Similar works

Full text

Available Versions

CERN Document Server