A GEANT-based Monte Carlo Code has been developed to model the performance of the central tracker for an SSC experiment. A summary of the current capabilities of the Monte Carlo will be presented followed by a more complete description of the results of a study of the details of detector performance

Kinnison, W.W.

Lee, D.M.

Ziock, H.J.

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LA-UR -9o-3694 _:)/Uff...-_. _/_/_--'/0Los Alamos Nahonal Laboratory =s operated by the University of California for the United States Departrnent of Energy under contract W-7405-ENG-3t,LA-UR--90-3694DEgl 001805TITLE: MUNTE CARLO SIMULATIONS OF A CENTRAL TRACING SUB-.£YS!'EMAUTHOR(S): vJ. W. Kit;nison.,D. Y., L,_e, H..-J,.,ZiockSUBMITTEDTO: Symposium on Detector Research and Development for theSuperconducting Super Collider, Fort Worth, Texas,October 15-18_ 1990DISCLAIMERThis report was prepared as an account of work sponsored by an agency of the United StatesGovernment. Ncither the Unite,d States Government nor any agency thereof, nor any of their _' i _ i_i i_ "employees, makes any warranty, express or implied, or assumes any legal liability or responsi- ; _!,_! !_; _ ,,,bility for the a"curacy, completeness, or usefulnessof any information, apparatus, product, or i _,_ : .;' {l(:.,,.j: , _; _)[_'": 'process disclosed, or represents that its use would not infringe privately owned rights. Refer- ) "¢nc¢ herein to any specific commercial product, process, or service by trade name, trademark, NOV 0 5 1990'manufacturer, or otherwise does not ncce_arily constitute or imply its endorsement, recom-mendation, or favoring by the United States Government or any agency thereof. 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A summary of the current capabilities of the Monte Carlo will be presentedfollowed by a more complete description of the results of a study of the details of detector performance.Introduction Currently, the code can model the performanceof a silicon tracker, a straw tube tracker and aVarious studies of the tracking criteria of a scintillating fiber sub-system. In addition, some in-generic SSC detector have been carded out at pre- formation has been included to take into account anvious workshops. Ali of those studies have used electromagnetic and hadronic calorimeter, as wellevent-level Monte Carlos. By that we mean that as, muon chambers and a magnet. On the otherthey have used as input the resolution functions of hand, the elements other than the central tracker arevarious detector components, the central tracker for far less developed and we will therefore only de..instance, and determined what the physics capabili- scribe the central tracking simulation in this report.ties of a detector with those resolution functions The first step of the program is the simulationwould be. Our group at Los Alamos has recently section. The simulation includes all active elementsput together a fairly detailed Monte Carlo of a de- of each sub-system of the detector as well as a verytector which begins at the component level. The close approximation of the amount and location ofinitial purpose of the new Monte Carlo was to support structures. Via GEANT, ali physics pro-derive (and therefore validate) the resolution func- cesses of charged and heutral particle interactionstions which had previously been used in event level with material are taken into account. The energystudies. More recently, it has been used to study is- loss in active detector elements is recorded and,sues of data acquisition and triggering techniques after ali tracks have been followed through thewhich are inaccessible to an event level code. entire detector, it is digitized. That latter processThis report will describe the new Monte Carlo results in an output which is very much like a databy first describing the current capabilities and some tape. For the silicon we record the strip numberof its general features. Next, results of resolution and the width of the cluster of contiguous struckfunctions and detector performance which will strips and time of the strip fh'ing, in units of 16 nsimpact the design of the data acquisition system for each digitization. In that process, only stripswill be summarized, which have a total of 20 KeV of energy depositedare assumed to have f'trt'd their discriminators andScope of the Program are thus recorded. For the straws, the equivalentTDC output is recorded (with respect to the beamThe Monte Carlo program that we have devel- crossing) and for the scintillating fibers we recordoped is based upon GEANT 3 [1] and uses which fiber was struck, i.e. we assume that onlyISAJET 6.25 [2] for event generation. Both the discriminators are connected to the fibers. In aligeometry specifications as well as which parts of cases, an appropriate time evolution, including driftthe code are active during a run are determined by times, particle flight times, and propagation timesinput data cards. These two features are quite use- down long sense wires are taken into account.ful since the exact specifications of detector sub- The results of the simulation can be recordedcomponents can be quickly modified in order to try on disk as a file tape or immediately input into thedifferent geometries. Also, entire sub-detectors of second section, the event reconstruction section.the SSC detector can be left out in order to focus The event recons_:ction follows the same schemecomputer time on the parts which are relevant for a as would be used in most high-energy colliderparticular study, experiments. That is, for each detector sub-system,coordinates are recreated from the digitized data, hadronic interactions in the silicon wafers. Thispa'ttern recognition is performed to get track quantity is independent of whether it is normal datacandidates from sub-systems of similar resolutions or jet data. The second quantity which will be ofwhich are then fit, and finally, tracks from the importance in the re,,d-out design is the number ofvarious sub-systems are joined for particle identifi- clusters per 18-cm long by 3.2-cm wide wafercation and kinematic event analysis. In our current which is shown in Figure 2. These data, combinedscheme, tile results of tile track fit in the silicon is with the information that there are approximatelyused to do primary and secondary vertex finding, 10 charged particles per unit of rapidity perbut the particle identification phase is incomplete interaction provide information on the amount ofdue to not having a full model of the calorimeters data which are required to be read out or bufferedand muon chambers, for each SSC trigger in a given geometry.At each stage of the process, enough informa-tion is carried forward to allow a comparison of theresults with the generated Monte Carlo data. Inparticular, we can use the code to derive the coor- _ 1°_ ii1.dinate, vertex, and track fitting resolutions whichcan be i;:put into event level simulators to helpdetermine the physics capabilities of the detector. _ 10_(Note that that could also be done with this MonteCarlo and not resort to event-level simulation bygenerating enough events of a specific type, but at 102the current level of SSC detector designs, eventlevel simulation is a more practical approach.) Wecan also use the digitization results themselves to 10provide information which is relevant to the suit-abil ty of various data acquisition schem s. __h_1 ISimulation, Results 0 a_ 4o _ 80 100Strips/ClusterThe results which are summarized here use the Figure 1:The numberof contiguousstripsin eachclusterdetector geometry from the Silicon Tracker Sub- readout ofthe silicondetector.system R&D collaboration. (See Reference 3 for adescription of that geometry.) Two types of datawere used in the simulations. The first type is data_ Nomal Datawhich have a Higgs event (MH = 300 Ge V/c2) _0,along with a number of overlapping minimum bias ,---, JetDataevents corresponding to a luminosity of 1033.(Actually, the overlapping events were 4 GeV PT 1°_two-jet events.) In the discussion which followswe call these normal data. The second kind of data,which we call jet data, were high PT two-jet _°2events, again overlapping minimum-bias eventswere included. The normal data were to study theperformance of the detector for the majority of thetriggers (the Higgs tracks do not perturb those ,nrl.results), and the second set were to examine how 1 ! 11the detector performs when there are a large .. I'l... ,.,..number of very nearby tracks. We will first present 0 10 20 _ 4o _0the component level performance which will have Clusters/Waferan impact upon data acquisition followed by the Figure2: The numberof clustersper waferforboth normalhigher level resolution function data. and jet data. Even in jet data only 3.3% of the events haveFigure 1 shows the distribution of the number morethan threeclustersperwafer.of strips which are above threshold for each con- Next one must answer the question of how thetiguous cluster of strips of the silicon detector, resolution is affected by the overlap of the clusterThough the distribution has an average of 3.4 one from one track with that of a nearby track. Figure 3can see there are long tails primarily arising from shows the number of actual tracks which are foundto contribute to a cluster used for coordinate finding at least 8 of the ten possible coordinates perreconstruction. The actual number of times that two track correctly. The algorithm that we use for theor more tracks contributed to the same cluster is individual vector finding attempts to have a low Pr1.3% for normal data and 2.7% for data in jets. cut..off of 300 MeV and that is the source of theConsequently, one would expect that the normal low efficiency below about 500 MeV PT. The effi-estimate of the individual coordinate resolution ciency levels off at about 94% due to the individualwould be e close approximation to the actual coordinate efficiency.prediction of the Monte Carlo. That is indeed thecase. The Monte Carlo predicts that in the barrel ofthe detector the r-tp resolution is a square distri-bution with a width equal to the pitch (50 ktm in 1.0our simulation) and the z resolution in the barrel isa triangular distribution with a base of 1 cm (which _ o.8comes from the 5 mrad stereo angle and 50 ktmpitch). Similarly, in the forward detector, the tp- _ 0.6angle is associated With the 50 ktm pitch and theradial resolution of the coordinate has a triangulardistribution with a base of 1 cm. The silicon detec- _ o.4tor has an inefficiency due to a 0.5 mm dead regionaround the edges of every wafer and a triangular _ 0.2dead region due to the stereo read-out. In ourMonte Carlo, those factors contribute to a 97%efficiency per coordinate. 0.0 ..................................0 1 2 3 4 5 6 7 8 9 10PT(GeVlc)Figure 4: The pattern recognition efficiency as a function ofPl'. The efficiency shown is for f'mding 8 good coordinatesm _ Data out of the 10 possible coordinates for the silicon trackerao4 _ Jet t_t_ subsystem.ao_ , ConclusionsIn conclusion, we have developed a Monteao2 Carlo code which has been use to begin a study ofcomponent level simulations of a .silicon trackersub-system. Its primary utilization would be to10 provide input into event level .simulators and studyissues of importance to data acquisition and pattemrecognition.o 4 8 12 _6 2o 24 ReferencesTracks/CoordinateFigure 3: The number of tracks which contributed to each [1] R. Brun, et al., "Geant 3", CERN Reportreconstructed coordinate. In an ideal detector only one track CERN DD/EE/84-1.would contribute to each reconstructed coordinate.[2] F.E. Page, et al., "ISAJET 6.25", CERNAs a last item, we will report on the pattern Pool W999.recognition capabilities of the tracker that we haveinvestigated. In our approach, we construct vectors [3] W. Miller et al., "Mechanical Engineeringmade up of coordinates from successive layers of and Design of Silicon-Based Particlethe detector. From those vectors, a local estimate of Tracking Systems", these proceedings.tpO (the initial direction of the track in the r-tp[4] Alan Weinstein, private communication.plane), _c(the curvature) and coso of the track canbe estimated [4]. One then searches for clusters ofpoints in that 3-space to find associations of coor-dinates to tracks. Figure 4 shows the efficiency 6f

Monte Carlo simulations of a central tracing sub-system

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Monte Carlo simulations of a central tracing sub-system

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