1 / 35

Primary and secondary vertex finding

This article explores the role of charged particle tracking detectors in finding primary and secondary vertices. It delves into defining primary and secondary vertices, measuring impact parameter, and improving momentum resolution. Various aspects of tracking detectors, such as reconstructing charged particle tracks, assigning momentum and charge, and evaluating performance metrics like track finding efficiency and momentum resolution, are discussed. The text includes simulations from the ALICE experiment, emphasizing techniques for tracking efficiency and vertex reconstruction. It outlines methods for optimizing cuts and identifying secondary vertices, with examples such as strangeness detection and K0(892) resonance reconstruction in different collision scenarios.

visser
Download Presentation

Primary and secondary vertex finding

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Primary and secondary vertex finding

  2. Role of charged particle tracking detectors Inner Tracker Detectors Define primary and secondary vertices Measure the impact parameter of the tracks Define initial direction of the track Improve momentum resolution Tracking detectors Reconstruct the overall track of all charged particles Assign each track momentum and charge sign

  3. Performances evaluation Track finding efficiency Momentum resolution Impact parameter resolution Angular resolution Secondary vertices reconstruction

  4. Track finding efficiency Usually depends on many factors (multiplicity, particle species, pT,…) ! From comparison to the known tracks, define: Findable tracks Good tracks: reconstructed tracks wich correspond (within some criteria) to true tracks Fake tracks: reconstructed tracks which do not correspond to true tracks Simulations from ALICE Tracking efficiency = Found/Findable

  5. Angular resolution Simulations from ALICE

  6. Overall figure of merit for tracking Simulations from ALICE

  7. Needs for tracking detectors Use magnetic field to bend charged particles Deposit some energy into detectors to have point tracks Do not disturb particle trajectories

  8. Reconstruction of primary vertex Case study: primary vertex finding in ALICE by two layers of silicon pixel detectors A PbPb central event produces in the first layer a multiplicity of the order of 30000. Zv= position of vertex along z

  9. Reconstruction of primary vertex Find approximate estimate of vertex position by z-distribution in Layer 1 Layer 1 Z-axis

  10. Reconstruction of primary vertex For vertex position not too far from z=0, correlation with the true vertex position

  11. Reconstruction of primary vertex Use a polinomial fit to estimate first approximation vertex position =a+b• Zv0+c •(Zv0)2

  12. Reconstruction of primary vertex • Correlate positions in the two layers introducing some cuts on (z1-z2) and (f1 , f2 ) to reduce combinatorial background r (Z2, f2) (Z1, f1) r2 r1  Zv Zv0 Zvmin Zvmax Z Zvmin,max =Zv0 

  13. Reconstruction of primary vertex

  14. Reconstruction of primary vertex

  15. Reconstruction of primary vertex

  16. Reconstruction of primary vertex

  17. Reconstruction of primary vertex

  18. Reconstruction of primary vertex

  19. Reconstruction of primary vertex

  20. Reconstruction of primary vertex

  21. Reconstruction of primary vertex In pp collisions (small number of tracks) the primary vertex may be reconstructed after tracking. Each track is approximated with a straight line to the nominal vertex position. All possible track pairs (i,j) are considered and the center of the segment of minimum approach between the two lines is found Finally, the position of the vertex is found by minimization of V=covariance matrix of r

  22. Reconstruction of primary vertex Reconstruction of primary vertex in pp collisions (ALICE pixel detectors) from tracks

  23. Reconstruction of a secondary vertex Dpn = distance of closest approach Sec. vertex Dpn Radial distance Primary vertex

  24. Reconstruction of a secondary vertex Identification of a cascade weak decay in the central region of ALICE A fraction of a central Pb-Pb event in the ALICE central detector

  25. Reconstruction of a secondary vertex Event display of a D0-> K-π+ decay through the ALICE ITS detector

  26. Reconstruction of a secondary vertex

  27. Reconstruction of a secondary vertex • Example: Strangeness detection • Particle Decay B.R.(%) c (cm) • K0s + - 68.6 2.68 •  p - 63.9 7.89 • - - 99.9 4.91 • -  K- 67.8 2.46

  28. Reconstruction of a secondary vertex • In the reconstruction of a secondary vertex, a “good” found vertex must be: • Inside the fiducial volume • Within a certain window in the invariant mass • Coming from a real decay • Usual cuts applied: • Impact parameter of the 2 tracks • Distance of closest approach • Cos(θp) pointing angle between momentum of secondary track and vector connecting primary and sec. Vertex • The quality of applied cuts is checked by the Signal/Noise ratio S/B

  29. Reconstruction of a secondary vertex λ reconstruction in ALICE (300 HIJING events More stringent cuts on DCA and cos(θp)

  30. Reconstruction of a secondary vertex pp events in ALICE

  31. Reconstruction of a secondary vertex pp events in ALICE

  32. Reconstruction of a secondary vertex How to optimize cuts, comparing signal and background from simulations

  33. Reconstruction of a secondary vertex True signal Reconstruction of the K0(892) resonance in ALICE pp collisions Assumed: perfect PID pp events in ALICE

  34. Reconstruction of a secondary vertex Reconstruction of the K0(892) resonance in ALICE pp collisions Assumed: realistic PID Signal after subtraction of combinatorial background

  35. Reconstruction of a secondary vertex Reconstruction of the K0(892) resonance in ALICE Pb-Pb collisions Assumed: realistic PID

More Related