Framework for track reconstruction and it’s implementation for the CMS tracker A.Khanov,T.Todorov,P.Vanlaer

8 Feb 2000CHEP CMS/Track Reconstruction. Abstract A295. T.Todorov2 Problem Complexity CMS Tracker l About detector units l About 20M channels l About 50K hits per event (at nominal luminosity) l Homogeneous structure

8 Feb 2000CHEP CMS/Track Reconstruction. Abstract A295. T.Todorov3 Motivation l We cannot implement the optimal track reconstruction algorithm right away There’s probably no one optimal algorithm but several,each optimized for a specific task è We need a flexible framework for developing and evaluating algorithms l The mathematical complexity of track finding/fitting often limits the number of developers The involved algebra is often localized in a few places è If we could encapsulate the involved algebra in a few classes and separate it from the logic of the algorithm it would make track finding easier for developers

8 Feb 2000CHEP CMS/Track Reconstruction. Abstract A295. T.Todorov4 Trajectory State l A basic object in tracking is the TrajectoryStateOnSurface (TSoS in short) l It fully describes a trajectory locally, i.e. it has è position è direction è curvature è error matrix surface

8 Feb 2000CHEP CMS/Track Reconstruction. Abstract A295. T.Todorov5 TSoS (cont’d) l Usual problems with defining such a class è Choice of parameterization(s) è Who is responsible for conversion from one parameterization to another, and from local (surface) to global reference frame? è Who is responsible for propagation (extrapolation) to other surfaces? l Our choice: è The TSoS is providing all useful parameterizations, and it is constructable with any of them, so it performs all conversions internally, and on demand. è Transformation Jacobians are not accessible è Propagation is done by a separate object, a Propagator

8 Feb 2000CHEP CMS/Track Reconstruction. Abstract A295. T.Todorov6 Propagator l Transforms any trajectory state to any surface, returning a new TSoS l Includes material effects l Is an interface for several concrete propagators, useable interchangeably è a fast propagator using surface geometry è an interface to GEANE for detailed propagation in GEANT3 geometries è a tool with functionality equivalent to GEANE will be needed for GEANT4 l Completely encapsulates the algebra, Jacobians are not accessible to clients

8 Feb 2000CHEP CMS/Track Reconstruction. Abstract A295. T.Todorov7 Abstract detector l Now that we have defined the basic vocabulary (TSoS), we can move to the main building blocks of a track reconstructor: è An abstract detector ( Det interface) p provides measurements compatible with a TSoS on demand and in an optimal way è A DetLayer that adds navigation capability p navigation connections between DetLayers are establiched by algorithm-specific NavigationSchool objects Det measurements( TSoS,MeasurementEstimator) DetLayer nextLayers(TSoS)

8 Feb 2000CHEP CMS/Track Reconstruction. Abstract A295. T.Todorov8 More components More components l Abstract measurement è allows combining measurements of different dimensionality l Updator è updates a TSoS with a measurement from the same surface p operates in the local frame of the Det surface l Seed Generator è Crates initial trajectory candidates (seeds) p seeds are just TSoS with a DetLayer* for navigation

8 Feb 2000CHEP CMS/Track Reconstruction. Abstract A295. T.Todorov9 Trajectory Builder l Now we have all components for a Trajectory Builder: è Layer navigation provides next DetLayers to query è DetLayers provide compatible measurements è Updator, well, updates the trajectory parameters using the measurements è Do it again… l All we have to specify is the logic: è How many candidates to consider on each layer? è When to drop a trajectory candidate? è How to handle ambiguities Starting seed (can be external) measurement Updated state Predicted State

8 Feb 2000CHEP CMS/Track Reconstruction. Abstract A295. T.Todorov10 Track Reconstructor Putting together a è SeedGenerator and a è TrajectoryBuilder and adding a è TrajectoryCleaner p to resolve ambiguous cases we get a TrackReconstructor! è Which we can combine with another TrackReconstructor and use again a TrajectoryCleaner to eliminate duplicate tracks and we get a more efficient TrackReconstructor! l Seeded, regional etc. reconstruction is simply a matter of using an appropriate SeedGenerator (e.g. from a Calorimeter cluster)

8 Feb 2000CHEP CMS/Track Reconstruction. Abstract A295. T.Todorov11 Present status l We have successfully implemented a classic Kalman filter track finder, fitter and smoother. This means we have at least one implementation for all the components described. è It us undergoing full validation for the Tracker l The reconstruction is extended to include the Muon system. This implies è implementation of Muon DetLayer è extension of the NavigationSchool to the Muon layers è use of appropriate propagators when crossing absorbers è optimized combinatorial logic l A Deterministic Annealing track fitting method is implemented and is being evaluated l An advanced Connection Machine - like Seed Generator is being implemented

8 Feb 2000CHEP CMS/Track Reconstruction. Abstract A295. T.Todorov12 Conclusions and Outlook l We have developed a friendly environment for the implementation and evaluation of track reconstruction algorithms l We have successfully implemented a classic Kalman filter algorithm in this environment. l We are implementing and evaluating other promising algorithms. l We will implement versions of some components specialized for electron reconstruction, trigger and test beam applications, etc.