Performance of PHOBOS Vertex Finders in 200GeV pp Collisions at RHIC - PowerPoint PPT Presentation

1 / 15
About This Presentation
Title:

Performance of PHOBOS Vertex Finders in 200GeV pp Collisions at RHIC

Description:

Single Event, Monte-Carlo. True = -3.5 cm, Reconstructed = -3.9cm ... Monte-Carlo. Eff,Acc,Pur 1 ... Monte-Carlo. Conclusions ... – PowerPoint PPT presentation

Number of Views:34
Avg rating:3.0/5.0
Slides: 16
Provided by: rich640
Category:

less

Transcript and Presenter's Notes

Title: Performance of PHOBOS Vertex Finders in 200GeV pp Collisions at RHIC


1
Performance of PHOBOS Vertex Finders in 200GeV pp
Collisions at RHIC
  • Richard S Hollis
  • University of Illinois at Chicago
  • For the PHOBOS Collaboration

2
Collaboration
Collaboration
ARGONNE NATIONAL LABORATORY Birger Back, Alan
Wuosmaa BROOKHAVEN NATIONAL LABORATORY Mark
Baker, Donald Barton, Alan Carroll, Nigel George,
Stephen Gushue, George Heintzelman, Burt
Holzman, Robert Pak, Louis Remsberg, Peter
Steinberg, Andrei Sukhanov INSTITUTE OF NUCLEAR
PHYSICS, KRAKOW Andrzej Budzanowski, Roman
Holynski, Jerzy Michalowski, Andrzej Olszewski,
Pawel Sawicki, Marek Stodulski, Adam Trzupek,
Barbara Wosiek, Krzysztof Wozniak MASSACHUSETTS
INSTITUTE OF TECHNOLOGY Maartin Ballintijn, Wit
Busza (Spokesperson), Patrick Decowski, Kristjan
Gulbrandsen, Conor Henderson, Jay Kane, Judith
Katzy, Piotr Kulinich, Jang Woo Lee, Heinz
Pernegger, Corey Reed, Christof Roland, Gunther
Roland, Leslie Rosenberg, Pradeep Sarin,
Stephen Steadman, George Stephans, Carla Vale,
Gerrit van Nieuwenhuizen, Gábor Veres, Robin
Verdier, Bernard Wadsworth, Bolek
Wyslouch NATIONAL CENTRAL UNIVERSITY,
TAIWAN Chia Ming Kuo, Willis Lin, Jaw-Luen
Tang UNIVERSITY OF ILLINOIS AT CHICAGO Russell
Betts, Edmundo García, Clive Halliwell, David
Hofman, Richard Hollis, Aneta Iordanova, Wojtek
Kucewicz, Don McLeod, Rachid Nouicer, Michael
Reuter, Joe Sagerer UNIVERSITY OF
MARYLAND Abigail Bickley, Richard Bindel, Alice
Mignerey, Marguerite Belt Tonjes UNIVERSITY
OF ROCHESTER Joshua Hamblen, Erik Johnson, Nazim
Khan, Steven Manly, Inkyu Park, Wojtek
Skulski, Ray Teng, Frank Wolfs
3
Detector
Phobos Detector
  • 4p Multiplicity Array
  • Octagon, Vertex and Ring Counters
  • Mid-rapidity Spectrometer
  • TOF wall for high momentum PID
  • Triggering
  • Scintillator Paddle Counters
  • Zero Degree Calorimeter (ZDC)
  • Cerenkov Counters

4
Octagon
High Multiplicity (AA) ? tracks through Vertex
Detector and Spectrometer Low Multiplicity
(PP)? too few tracks! need a different method
Octagon View of unrolled detector
Holes for Vertex Detector and Spectrometer.
Hits
Vertex from Monte-Carlo
5
Step 2
Step 1 Particles traversing more material will
deposit more energy this is the principle
behind the cosh(?) dependence (next slide).
q
Z, beam axis
6
Step 1
Step 2 Extract energy deposited in the octagon,
as a function of pseudorapidity.
Familiar cosh(?) dependence, lines for guidance.
7
Step 3 and 4
Step 3 Divide out the cosh(?) dependence ?
independence of energy with pseudorapidity at low
pseudorapidity ? smaller energy deposits at high
pseudorapidity.
Step 4 Apply a simple merging technique ? sum
energies at high eta
8
Step 5
Step 5 Make a cut on the data around the nominal
MIP position ? count the number of Mip Hits.
9
Iterations
Scan over possible vertices The process is
repeated for all possible vertex positions (-90
to 90 cm). ? best position is found for most
Mip Hits (for a given trial Z). This is due to
the correction applied being vertex dependent.
Single Event, Monte-Carlo
True -3.5 cm, Reconstructed -3.9cm
10
Eff,Acc,Pur 1
Accuracy, Purity, Efficiency Accuracy how close
to the true vertex Purity how many within 1
sigma (2cm) of truth. Efficiency how often is a
vertex found
11
Eff,Acc,Pur 2
Accuracy, Purity, Efficiency Accuracy how close
to the true vertex Purity how many within 1
sigma (2cm) of truth. Efficiency how often is a
vertex found
12
Eff,Acc,Pur 2
Accuracy, Purity, Efficiency Accuracy how close
to the true vertex Purity how many within 1
sigma (2cm) of truth. Efficiency how often is
a vertex found
13
Eff,Acc,Pur 2
Accuracy, Purity, Efficiency Accuracy how close
to the true vertex Purity how many within 1
sigma of truth. Efficiency how often is a
vertex found
14
Effect on Multiplicity
Effect of Vertex Accuracy on Multiplicity
Distribution
Black ? original (unsmeared) dN/dh
distribution Red ? simulates the mip-based vertex
finder (above) Green ? simulates the track-based
vertex finder (left)
Monte-Carlo
Uncertainty in Z ? shift in pseudorapidity Pea
ks at ? 2 ? more pronounced Dip at
midrapidity ? deeper
15
Conclusions
Conclusions
Vertex Finding Procedure Works. Accuracy ?
Strong Function of Hits ? Width 2cm ? Known
Effect on Pseudorapidity Density
Write a Comment
User Comments (0)
About PowerShow.com