Detector-related. related software development in the HEPP project. Are Strandlie Gjøvik University College and University of Oslo

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1 Detector-related related software development in the HEPP project Are Strandlie Gjøvik University College and University of Oslo

2 Outline Introduction The ATLAS New Tracking project HEPP contributions Summary

3 Introduction Track reconstruction before late 80 s usually divided into two separate subtasks track finding (pattern recognition) track fitting (estimation( of track parameters) Track finding starts out with all measurements in an event

4 Introduction Track finder identifies measurements potentially created by the same particle Track fit estimates track parameters and validates track candidate Raw data usually not seen in further analysis

5 Introduction Track finding in the old days: purely manual! Not plausible at the LHC.

6 Introduction Kalman filter introduced in late 80 s Traditional bondary between track finding and fitting less clear due to recurrent structure of the algorithm principle of Kalman filter, track finding and fitting interconnected

7 Introduction Post-Kalman Kalman-filter developments from late 90 s: Non-linear linear,, adaptive extensions of Kalman filter (Gaussian( Gaussian-sumsum filters, Deterministic Annealing Filter) Designed to cope with non-gaussian detector material effects (bremsstrahlung,, multiple scattering), track reconstruction at very high noise levels etc. Have more recently been proven successful also for vertex reconstruction Consensus today that there is no single algorithm suitable for all needs several different algorithms exist choice depending on noise level, particle type etc. Experiment software must therefore be flexible and modular allowing for easy exchange between different algorithms, depending on actual physics, noise level etc.

8 ATLAS New Tracking ATLAS reconstruction software traditionally dominated by (a few) monolithic packages one author per package difficult for others to contribute ATLAS New Tracking project initiated a few years ago make software more flexible and modular allowing and encouraging more developers has become the default approach in ATLAS

9 ATLAS New Tracking

10 ATLAS New Tracking

11 HEPP contributions Development of track parameter and covariance matrix transport engine in inhomogeneous magnetic fields and continuous material STEP propagator (E. Lund, UiO, supervision by L. Bugge, UiO, and A. Strandlie) Track reconstruction algorithm validation on cosmics data (A. Tonoyan,, UiB) Implementation of non-linear and adaptive track fitting algorithms Deterministic Annealing Filter (S. Fleischmann, Wuppertal, supervision by A. Strandlie) Gaussian-sum sum filter for electron track reconstruction (T. Atkinson, Melbourne, supervision by A. Strandlie) Development of ATLAS fast track simulation (A. Salzburger,, Innsbruck, initiated by A. Strandlie)

12 HEPP contributions New Tracking scheme for track parameter transport, navigation and material integration in ALL ATLAS sub-detectors: prediction of the trajectory by navigation in the TrackingGeometry between Volumes between Layers transport within volume by dedicated propagator (sub-detector dependent) Volumes and Layers carry material information: Navigation between Volumes: Navigation between Layers: Material interactions can be taken into account both ways. Layer based Volume based Model of ATLAS SCT Detector

13 HEPP contributions Most general propagator implemented in ATLAS: STEP (E. Lund, UiO) Deals with inhomogeneous magnetic fields and continuous material Based on adaptive Runge-Kutta Kutta-Nystrøm approach for track parameter transport Propagates covariance matrix in parallel (Bugge- Myrheim approach)

14 HEPP contributions comparison of STEP with other adaptive RK s,, as well as fixed-step approaches

15 HEPP contributions evaluating quality of covariance matrix transport by pull and p- value histograms

16 HEPP contributions energy loss in STEP compared to tables and Geant4

17 Tracking geometry in muon system (MTG), material integration using STEP as propagation engine, comparison to Geant 4 (S. Todorova,, Tufts)

18 HEPP contributions Comparison of New Tracking and Combined Test Beam algorithms on data from combined cosmic runs (A. Tonoyan,, UiB) using data from TRT barrel identifying problems of (at that time (~ 1 year ago) recently implemented) ) New Tracking algorithms problems have been fixed later Responsibility for simulation for TRT and SCT endcap tests (A. Tonoyan,, UiB)

19 less hits and less tracks for New Tracking than for CTB tracking

20 Momentum resolution in Inner Detector as function of TRT noise (S. Fleischmann (+ A. Strandlie)) Deterministic Annealing Filter: iterated Kalman filter with annealing (inspired by statistical mechanics). Dealing efficiently with high noise levels fast simulation preliminary

21 HEPP contributions Electrons lose energy mostly by Bremsstrahlung Bethe-Heitler Distribution PDF f(z) = { ln () z } t ln(2) 1 Γ t ln(2) t = X X 0 = amount of material z = final Energy initial Energy

22 HEPP contributions Gaussian-sum sum filter resembles several Kalman filters running in parallel Different components correspond to various degrees of hardness of bremsstrahlung radiation Measurements used to a posteriori determine which component is correct T. Atkinson (+ A. Strandlie)

23 HEPP contributions Reconstructed invariant mass e + e - m J/Ψ = GeV Full width Γ = 91.0KeV Invariant mass: m 0 2 =E 2 - r p 2

24 EVENT GENERATION 4-vector creation SingleParticle, PYTHIA, HERWIG... A. Salzburger et al. (initiated( by A. Strandlie) Full Detector Simulation interaction with detector material, hit creation/particle decay Geant4, FLUKA, Digitization signal + noise creation Event Data Preparation Simulation EDM Trajectory Creation uses the Extrapolation Tool and Geometry of Reconstrucion EDM Track/Noise Creation uses the Fitting/Data preparation Tools of Reconstrucion Fast Detector Simulation parametric smearing of track parameters according to obtained smearing functions EDM Track Finding EDM Track Finding ATLFAST EDM Track Fitting EDM Track Fitting Event Data Preparation EDM Analysis, Data Persistency, Event Visualization