Calorimeter-assisted tracking with the SiD Detector at the ILC

1. Calorimeter-assisted tracking with the SiD Detector at the ILC Dima Onoprienko, Eckhard von Toerne Bonn University / Kansas State University 30th October 2006 Eckhard von Toerne

2. SiD - Rationale • SiD is one of three Detector concepts under study (GD, LD, SiD) for the International Linear collider (ILC). SiD Design Study Homepage: http://www-sid.slac.stanford.edu/ • SiD supports event kinematics reconstruction with particle flow algorithms • Jet energy resolution goal is DE/E = 30%/√E [GeV] • Dense highly segmented electromagnetic and hadron calorimeters • Excellent momentum resolution, precise vertex identification, and robust performance are required from the tracker. • SiD design – starting point for optimization : • 5 layer silicon pixel vertex detector • 5 layer silicon strip air-cooled outer tracker • Highly segmented Silicon-Tungsten EM calorimeter • Steel / RPC hadron calorimeter inside the coil. • 5 Tesla magnet • Instrumented flux return Eckhard von Toerne

3. Particle Flow Algorithms in a Nutshell Measure the energy deposition of every particle, not the energy deposited in calorimeter modules. Forcharged tracksuse trackermomentum measurement instead of calorimeter entry. High transverse and longitudinal segmentation is needed to distinguish individual particles. Eckhard von Toerne

4. Why do we need 30%/Sqrt(E [GeV]) DE/E = 30%/Sqrt(E [GeV]) makes W-Z separation possible Eckhard von Toerne

5. SiD Starting Point • 5 layer pixel VXT • 5 layer Si tracker with endcaps • Si/W Ecal and Hcal inside the coil • 5T Solenoid • Instrumented flux return for muons detection Compact: 12m x 12m x 12 m SiD is moving beyond the starting point, with subsystem designs, full G4 subsystem descriptions, pattern recognition and PFA code development, and benchmarking studies. Eckhard von Toerne

6. SiD Tracking High precision vertex detector (silicon pixels, 5 layers) “Thin” tracker (silicon strips) Compact finely segmented EM calorimeter (silicon/tungsten) Excellent momentum resolution,pt/pt2 ≤ 5 x 10-5GeV-1 targeted Tracker pattern recognition relies on seeds from the vertex detector ( >95% Efficiency if seed hits available ) There is a class of tracks for which this does not work : long lived particles (K0S , Λ , exotic) often decay outside the vertex detector. SiD Event Display But we have a finely segmented EM calorimeter – can start from there, using MIP stubs as seeds. Eckhard von Toerne

7. Tracking Efficiency Vertex-Seeded Tracking Efficiency ~ 95% The rest is K0’s and ’s and long lived charm and b’s. Additional Tracking algorithms for those. Standard tracking  = p/2 – polar angle  Results of prelimary study without vertex detector forward disks Eckhard von Toerne

8. KS0 decay radius in XY plane (cm) Most K0s  pi+ pi- decay daughters do not leave enough hits in Vertex detector to allow standard track reconstruction 3 cm Reconstruction of Long-lived Particles with the SiD • Example: K0s • Most K0s particles decay at a Radius greater 3cm and lack the VXD hits for standard track reconstruction • Reconstruction of K0 with calorimeter-assisted tracking K0s +- reconstructed with KSU algorithm (GarfieldTracking) Eckhard von Toerne

9. Calorimeter Assisted Tracking Algorithm Run standard tracking and clustering algorithms. Identify EM calorimeter clusters and tracker hits that are not associated with any reconstructed tracks. Find MIP stubs and calculate position, direction, and curvature radius for each of them. Extrapolate tracks from MIP stubs towards the center of the detector, picking up tracker hits as we go. After each new added hit, recalculate track parameters (Chi2-Fit). If there are multiple hit candidates in the same layer, branch and create new tracks. Apply quality cuts to tracks, discard duplicates. Find track intersections, reconstruct original particle. Eckhard von Toerne

10. Running "Garfield" track finder on hadronic events KS0 → π+π- M(pp) [GeV] Eckhard von Toerne

11. L-peak Width = 2.2 MeV 2 MeV Lambda - Peak p- Based on single-Lambda sample Eckhard von Toerne

12. A few K0s+- examples A Curler – curled track is not reconstructed Eckhard von Toerne

13. "Garfield" track finder package – current status The package has been ported to the LCIO-based org.lcsim framework, and interfaced to all standard analysis tools used by the SiD study groups. Decoupled from a particular geometry. Supplemented by performance analysis package. Modified to allow use of alternative algorithms for some of the reconstruction steps. The package is being used for physics reach, detector optimization, and tracking performance studies by many groups (UC Santa Cruz, DESY, University of Colorado, Laboratoire d'Annecy-le-Vieux de Physique des Particules, SLAC, KSU, …) Eckhard von Toerne

14. What else can we do with this algorithm ? Exotic events Kinked tracks Calorimeter backscatters Eckhard von Toerne

15. Reconstructing Long-Lived Exotic Particles e+e- → c*0c*0 c*0 → c0m+m- Eckhard von Toerne

16. Impact of long-lived particles on Particle Flow • Back of envelope calculation: • 10% of hadrons are strange, • ~35% of that are  or K0s • 2/3 of decays yield charged tracks contribution of V0 decays to total hadronic energy • Important is impact on energy measurement uncertainty not total contribution to particle flow • Even if V0 decay is not reconstructed, clusters from the tracks are. 10% 3.5% 2% Eckhard von Toerne

17. Particle Flow Activities – Work in progress and plans • Moving from "proof-of-principle" to "industrial strength" implementation Switch to using standard framework objects everywhere • Improve efficiency in 500 GeV hadronic events • MIP stub finding - our own and PFA options (Argonne, Northern Illinois University, University of Iowa) • Hit cleanup with vertex-based or standalone finders • Fake track suppression • Integrate with Particle Flow Algorithms • Investigate tracking in forward region • Problematic region for other algorithms, within our reach due to excellent angular coverage of the EM calorimeter Eckhard von Toerne

18. Thanks to John Jaros, Harry Weerts, Marcel Demarteau Norman Graf, Tim Nelson, Steve Wagner Eckhard von Toerne

19. BACKUP Eckhard von Toerne

20. Effects of Barrel Tiling on Tracking No tiling in Barrel 10-cm tiling in Barrel Caveat: No additional material taken into account Better Resolution, slightly higher efficiency Eckhard von Toerne

21. Cluster-Track Matching Important for Particle Flow algorithms Provide a list of ECAL clusters associated to tracks Merge Standard tracks with our tracks? Eckhard von Toerne

22. Standard Tracking With GarfieldTracking GarfieldTracks Highlighted Eckhard von Toerne

23. R 1.27 m CAD overview ECAL • 20 layers x 2.5 mm thick W • 10 layers x 5 mm thick W • ~ 1mm Si detector gaps • Preserve Tungsten RM eff= 12mm • Highly segmented Si pads 12 mm2 John Jaros Eckhard von Toerne

24. LCIO Persistency Framework Generator Analysis Recon- struction Simulation Simulation Framework LCIO. • Flexible geometry description with interface to reconstruction and analysis packages • Modular design allowing “mix-and-match” approach to choosing packages and algorithms anywhere in the chain from event generation to data analysis. • Common data model and input-output framework. LCIO : Linear Collider Input-Output Framework geometry Eckhard von Toerne