Npart Determination for √ sNN = 200GeV d+Au Collisions
in
Aneta IordanovaUniversity of Illinois at Chicago
2003 DNP Fall Meeting, Tucson, Arizona
Collaboration
Birger Back, Mark Baker, Maarten Ballintijn, Donald Barton, Russell Betts, Abigail Bickley, Richard Bindel, Wit Busza (Spokesperson), Alan Carroll, Zhengwei Chai, Patrick Decowski, Edmundo Garcia, Tomasz Gburek, Nigel George, Kristjan Gulbrandsen, Stephen Gushue,
Clive Halliwell, Joshua Hamblen, Adam Harrington, Conor Henderson, David Hofman, Richard Hollis,
Roman Hołyński, Burt Holzman, Aneta Iordanova, Erik Johnson, Jay Kane, Nazim Khan, Piotr Kulinich, Chia Ming Kuo, Willis Lin, Steven Manly, Alice Mignerey, Gerrit van Nieuwenhuizen,
Rachid Nouicer, Andrzej Olszewski, Robert Pak, Inkyu Park, Heinz Pernegger, Corey Reed, Michael Ricci,
Christof Roland, Gunther Roland, Joe Sagerer, Iouri Sedykh, Wojtek Skulski, Chadd Smith, Peter Steinberg, George Stephans, Andrei Sukhanov, Marguerite Belt Tonjes, Adam Trzupek, Carla Vale, Siarhei Vaurynovich, Robin Verdier, Gábor Veres, Edward Wenger, Frank Wolfs,
Barbara Wosiek, Krzysztof Woźniak, Alan Wuosmaa, Bolek Wysłouch, Jinlong Zhang
ARGONNE NATIONAL LABORATORY BROOKHAVEN NATIONAL LABORATORYINSTITUTE OF NUCLEAR PHYSICS, KRAKOW MASSACHUSETTS INSTITUTE OF TECHNOLOGYNATIONAL CENTRAL UNIVERSITY, TAIWAN UNIVERSITY OF ILLINOIS AT CHICAGO
UNIVERSITY OF MARYLAND UNIVERSITY OF ROCHESTER
What is Npart ?
IMPORTANCE: connects experiments with theoretical models
UNFORTUNATELY: cannot be measured directly!
Deuteron (d)
Gold (Au)
Parameters of Nuclear Collisions: Impact parameter b Number of participants Npart
Number of collisions Ncoll
others …
Participants
Spectators
b
How do we Measure Npart?
Assumptions: Some measured quantities in the DATA correctly reflect the collision geometry !
Choose the quantity wisely (talk by Richard Hollis)
I chose summed hits in -3 < < 3
Big Step:
Map the quantity in MC to the unknown Npart
Associate same MC/Data to the same Npart
Experimentdata Theory
models
EOct
ηSchematic Plot
not to scale
Hijing Monte-Carlo Studies
Unbiased EOct distribution– represents the full geometrical cross section
Slice this distribution into percentile bins
EOct
Npa
rt
Peripheral → Central
0-10%10-20%40-60%100-80% EOct
Cou
nts
Percentile Bins for Data DATA measured cross section
MC distribution with trigger and vertex bias
Data and MC (biased) distributions match well
Data cut = MC cut X scale factor
Extract <Npart> for each bin in biased MC distribution
Scale
Normalize
Found <Npart>
Central → Peripheral Central → Peripheral
final
<Npa
rt>
<Npa
rt>/
<Npa
rt>,
RM
S/<N
part
>
% %% Cross Section % Cross Section
Are we SURE!Systematic errors on <Npart>
Trigger Bias : Accounts for the missed part of the cross section
Is our Trigger simulated properly?
Vertex Bias :
- Compared MC (Trigger +Vertex Bias) with MC (no bias)
- only peripheral bins affected
Overall Efficiency → Shape matching
- from Data/Hijing MC (82%)
- from Data/Glauber MC (upper limit on how wrong we could be) 10% error
<Npart> from (MC) EOct distribution
<Npart> from Npart distribution
Errors due to smearing: Simulated EOct has effects from electronic noise and spatial (vertex) distribution
slice MC Npart distribution in %-le bins (<Npart> “true” result )
%
Central → Peripheral
<Npa
rt>
% Cross Section
Different ModelsHIJING
Uses Monte Carlo similar to Glauber multiple scattering model to calculate Npart
We used 2 HIJING versions with different nuclear density profiles for the deuteron
- Woods-Saxon (1.381)
- Hulthen (1.383)
(small difference between the versions)
Hulthen – Woods-SaxonWoods-Saxon
%% Cross Section
% <
Npa
rt>
Diff
eren
ce
Gives us the upper limits in our systematic studies
Only Npart available
Very difficult to introduce smearing which looks like EOct
All Studies Follow Steps:
Match HIJING and Glauber distributions
Use cut positions from HIJING
Find <Npart> from Glauber MC
Apply error to <Npart> from HIJING
Glauber MC
Distributions do not match well!<Npart> for the most central and peripheral bins is different compared to HIJING
Not Realistic → Must have Smearing
Glauber MC (Npart-2)HIJING EOct
Case1: No smearing (use Npart)
Glauber MC (Npart-2) + pGHIJING EOct
Introduce some smearing G ~ √Npart *Gaus(0,1)
Case2:
Scan for different parameters pSmearing closer to HIJING EOct for p~1
<Npart> still deviates from HIJING for the peripheral bin.Should include the Trigger Bias
Glauber MC p1 Npart + p2 G + p3 Npart4/3
HIJING EOct
Add additional scaling term ~Ncoll
Scan for different parameters(many more scaling and smearing function tested)
Case3:Add Trigger + Vertex Bias
Affects low centrality <Npart>
HIJ
ING
with
bia
s / H
IJIN
G
Peripheral bins with bias have larger <Npart> (+ 5%, 3%, 1%) compared to the unbiased cases
Case4:
Npart
Centrality Bin
ErrorsGlauber
ErrorsEfficiency
Final
100-80 14 16 22
80-60 11
60-40 7
40-20 5
20-10 5
10-0 4 4 6Cen
tral →
Per
iphe
ral
Final systematic errors on Npart
- combined studies from HIJING and Glauber MC
Example
Preliminary
<Npart> =8.1 ± 0.7(syst)
Min bias PseudorapidityDensity Distribution
PSM:D.Kharzeev et al,hep-ph/0212316
RQMD:H.Sorge,Phys.Rev C52 3291 (1995)
Preliminary
Min bias PseudorapidityDensity Distribution
AMPT:Zi-wei Lin and Che Ming Ko,nucl-th/0301025
HIJING:M.Gyulassy and X.N.Wang,Comp.Phys.Comm. 83 307 (1994)v. 1.381 (standard settings)
<Npart> =8.1 ± 0.7(syst)
Conclusion
We can measure <Npart> in d+Au
Have estimated the uncertainties using HIJING and Glauber MC
This is a different approach than Au-Au
First results from d+Au √sNN = 200 GeV Pseudorapidity Density Distribution
Prel
imin
ary
Min
-Bia
s P
seud
orap
idity
Den
sity
Dis
tribu
tion