Npart Determination for √ sNN = 200GeV d+Au Collisions

in

Aneta Iordanova

University 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

Np

art

Peripheral → Central

0-10%10-20%40-60%100-80% EOct

Co

un

ts

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

<N

par

t>

<N

par

t>/<

Np

art>

, R

MS

/<N

par

t>

% %% 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

<N

par

t>

% Cross Section

Different Models

HIJING

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-Saxon

Woods-Saxon

%% Cross Section

% <

Np

art>

Dif

fere

nce

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) + pG

HIJING 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

wit

h b

ias

/ H

IJIN

G

Peripheral bins with bias have larger <Npart> (+ 5%, 3%, 1%) compared to the unbiased cases

Case4:

Npart

Centrality Bin

Errors

Glauber

Errors

Efficiency

Final

100-80 14 16 22

80-60 11

60-40 7

40-20 5

20-10 5

10-0 4 4 6Cen

tral

→ P

erip

hera

l

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

Pre

lim

inar

y

Min

-Bia

s P

seud

orap

idity

Den

sity

Dis

trib

utio

n