N part Determination for √ s NN = 200GeV d+Au Collisions in

1. Npart Determination for √ sNN = 200GeV d+Au Collisionsin Aneta Iordanova University of Illinois at Chicago 2003 DNP Fall Meeting, Tucson, Arizona

2. 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 LABORATORY INSTITUTE OF NUCLEAR PHYSICS, KRAKOW MASSACHUSETTS INSTITUTE OF TECHNOLOGY NATIONAL CENTRAL UNIVERSITY, TAIWAN UNIVERSITY OF ILLINOIS AT CHICAGO UNIVERSITY OF MARYLAND UNIVERSITY OF ROCHESTER

3. What is Npart ? Deuteron (d) Participants b Spectators Gold (Au) Parameters of Nuclear Collisions: Impact parameter b Number of participants Npart Number of collisions Ncoll others … IMPORTANCE: connects experiments with theoretical models UNFORTUNATELY: cannot be measured directly!

4. EOct How do we Measure Npart? Experiment data Theory models • 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 < h < 3 • Big Step: • Map the quantity in MC to the unknown Npart • Associate same MC/Data to the same Npart Schematic Plot not to scale η

5. 100-80% 40-60% 10-20% 0-10% Hijing Monte-Carlo Studies Counts Npart EOct • Unbiased EOct distribution– represents the full geometrical cross section • Slice this distribution into percentile bins EOct Peripheral → Central

6. Percentile Bins for Data DATA measured cross section Normalize MC distribution with trigger and vertex bias Scale • Data and MC (biased) distributions match well • Data cut = MC cut X scale factor • Extract <Npart> for each bin in biased MC distribution

7. Found <Npart> final Central → Peripheral Central → Peripheral <Npart> <Npart>/<Npart>, RMS/<Npart> % % % Cross Section % Cross Section

8. 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

9. 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 ) <Npart> Central → Peripheral % % Cross Section <Npart> from (MC) EOct distribution <Npart> from Npart distribution

10. Hulthen – Woods-Saxon Woods-Saxon % <Npart> Difference 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) % % Cross Section

11. Glauber MC • 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

12. Glauber MC(Npart-2) + pG HIJINGEOct Case1: Case2: No smearing (use Npart) Introduce some smearing G ~ √Npart *Gaus(0,1) Glauber MC(Npart-2) HIJINGEOct Distributions do not match well! <Npart> for the most central and peripheral bins is different compared to HIJING Not Realistic → Must have Smearing Scan for different parameters p Smearing closer to HIJING EOct for p~1

13. Add Trigger + Vertex Bias Case3: Case4: Add additional scaling term ~Ncoll Scan for different parameters (many more scaling and smearing function tested) Affects low centrality <Npart> HIJING with bias / HIJING Glauber MCp1 Npart + p2 G + p3Npart4/3 Npart HIJINGEOct <Npart> still deviates from HIJING for the peripheral bin. Should include the Trigger Bias Peripheral bins with bias have larger <Npart> (+ 5%, 3%, 1%) compared to the unbiased cases

14. Example Final systematic errors on Npart - combined studies from HIJING and Glauber MC Central → Peripheral

15. Preliminary Min bias PseudorapidityDensity Distribution PSM: D.Kharzeev et al, hep-ph/0212316 RQMD: H.Sorge, Phys.Rev C52 3291 (1995) <Npart> =8.1 ± 0.7(syst)

16. 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)

17. 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

18. Min-Bias PseudorapidityDensity Distribution Preliminary