How Strange is ? Current Results and Future Prospects

1. How Strange is ?Current Results and Future Prospects First RHIC Physics results! Mid-rapidity dN/dh for Central Au+Au George Stephans for the Phobos collaboration 24-July-Y2K Strangeness 2000

2. PHOBOS Collaboration ARGONNE NATIONAL LABORATORY Birger Back, Nigel George, Alan Wuosmaa BROOKHAVEN NATIONAL LABORATORY Mark Baker, Donald Barton, Mathew Ceglia, Alan Carroll, Stephen Gushue, George Heintzelman, Hobie Kraner ,Robert Pak,Louis Remsberg, Joseph Scaduto, Peter Steinberg, Andrei Sukhanov INSTITUTE OF NUCLEAR PHYSICS, KRAKOW Wojciech Bogucki, Andrzej Budzanowski, Tomir Coghen, Bojdan Dabrowski, Marian Despet, Kazimierz Galuszka, Jan Godlewski , Jerzy Halik, Roman Holynski, W. Kita, Jerzy Kotula, Marian Lemler, Jozef Ligocki, Jerzy Michalowski, Andrzej Olszewski, Pawel Sawicki , Andrzej Straczek, Marek Stodulski, Mieczylsaw Strek, Z. Stopa, Adam Trzupek, Barbara Wosiek, Krzysztof Wozniak, Pawel Zychowski JAGELLONIAN UNIVERSITY, KRAKOW Andrzej Bialas, Wieslaw Czyz, Kacper Zalewski MASSACHUSETTS INSTITUTE OF TECHNOLOGY Wit Busza*, Patrick Decowski, Piotr Fita, J. Fitch, C. Gomes, Kristjan Gulbrandsen, P. Haridas, Conor Henderson, Jay Kane , Judith Katzy , Piotr Kulinich, Clyde Law, Johannes Muelmenstaedt, Marjory Neal, P. Patel, Heinz Pernegger, Miro Plesko, Corey Reed, Christof Roland, Gunther Roland, Dale Ross, Leslie Rosenberg, John Ryan, Pradeep Sarin, Stephen Steadman, George Stephans, Katarzyna Surowiecka, Gerrit van Nieuwenhuizen, Carla Vale, Robin Verdier, Bernard Wadsworth, Bolek Wyslouch NATIONAL CENTRAL UNIVERSITY, TAIWAN Yuan-Hann Chang, Augustine Chen, Willis Lin, JawLuen Tang UNIVERSITY OF ROCHESTER A. Hayes, Erik Johnson, Steven Manly, Robert Pak, Inkyu Park, Wojtech Skulski, Teng, Frank Wolfs UNIVERSITY OF ILLINOIS AT CHICAGO Russell Betts, Christopher Conner, Clive Halliwell, Rudi Ganz, Richard Hollis, Burt Holzman,, Wojtek Kucewicz, Don McLeod, Rachid Nouicer, Michael Reuter UNIVERSITY OF MARYLAND Richard Baum, Richard Bindel, Jing Shea, Edmundo Garcia-Solis, Alice Mignerey

3. 13 June: 1st Phobos Collisions @ s = 56 AGeV 24 June: 1st Phobos Collisions @ s = 130 AGeV Relativistic Heavy Ion Collider

4. PHOBOS Apparatus

5. x z PHOBOS Trigger • Very loose coincidence of paddle counters (38ns) • Includes collision & background • Allows clean separation of collisions and background offline Positive Paddles Negative Paddles ZDC N ZDC P Au Au PN PP

6. For monitoring luminosity, background was rejected by requiring >2 hits in both of the paddles As soon as collisions appeared on the morning of June 13, we saw them Recorded 1000 collisions during that first night at s = 56 AGeV First Collisions at PHOBOS

7. Configuration used for first data SPEC: 6 planes of a single spectrometer arm VTX: Half of the Top Vertex Detector Paddles: 2 sets of 16 scintillators paddles Commissioning Run Setup Acceptance of SPEC and VTX

8. Examples of events Hits in SPEC Tracks in SPEC Hits in VTX 130 AGeV 130 AGeV Note: These events will give low p^ 56 AGeV

9. Variables & Observables • Variables: • Beam Energy • RHIC delivered s = 56 AGeV and 130 AGeV • Centrality of collision • Multiplicity in the paddles is related to number of participants, Npart • Observables: • dN/dh | |h|<1 ( where h = - ln tan (q/2) ) • Charged particle density averaged over –1 < h < 1 • dN/dh | |h|<1 /(Npart/2) • Particles produced per participant pair • (dN/dh | |h|<1 )130 / (dN/dh | |h|<1 )56 • Scaling of density with energy • Results presented will be for most central collisions

10. What do we learn from dN/dh | |h|<1 ? • Initial energy density in the collision • e is related to dN/dy • e.g. Bjorken estimate • dN/dy is related to dN/dh • Difference <5% CERN lab frame, <15% RHIC CM frame • We can also compare to pp, pp data • Energy scaling is sensitive to interplay between hard and soft processes dN/dy dN/dh

11. Monte Carlo Simulations • Event generator & detector simulation used for: • A proper description of all detector effects • Estimate of number of participants • We use the following packages: • HIJING 1.35 • Event generator for AA collisions • Hard processes, shadowing, jet quenching • GEANT 3.21 • Detector simulations • Production of secondaries in apparatus • Measured detector response • Derived from test-beam results • Generates fake data for silicon and paddle detectors

12. Collision Event Selection 1: Paddle Timing Dt < 8 ns selects events with vertex |z|<120 cm This sample still contains background events 2: ZDC Timing Dt < 20 ns confirms selected events as collisions However, at s=56 AGeV, this cut rejects ~10% of central collisions. At s=130 AGeV, the loss is <1%. 3: Paddle Multiplicity Requiring PP,PN to have a large ADC sum recovers central events lost to ZDC cut. Offline event trigger is 1 AND (2 OR 3)

13. Paddles cover 3<|h|<4.5 Sum of analog signals (gain-normalized) is proportional to the number of particles Secondaries deposit large amounts of energy. To reduce fluctuations, we use truncated mean Centrality Selection PP PN Hijing 130 AGeV b < 3 fm 3<|h|<4.5 h

14. 6% most central events based on paddles gives Extracting Npart MC Data Events/bin Npart Average paddle ADC sum Shaded region is most central 6% by paddle cut on both plots

15. Understanding Paddle Counters 56 AGeV 130 AGeV MC PP12 DATA PN12

16. Spectrometer sits very close to vertex High resolution tracking in 6 planes gives excellent vertex resolution z x Measuring Vertex: Procedure

17. Pointing accuracy describes how extrapolated tracks deviate from calculated vertex. Compares well with HIJING simulation Measuring Vertex: Results Track deviation at vertex Data: points MC: line

18. Beam Orbit can be calculated for each fill For the 130 AGeV data X = -.17 cm, sX = .17 cm Y = .14 cm, sY = .08 cm Vertex Distributions Y X • We make a cut in Z to define a fiducial volume Z

19. Critical test of detector understanding Both distributions contain the same number of central events Points are for VTX data No correction for detector thickness Histogram is for simulated VTX signals GEANT Response from test-beam Electronics noise Shulek correction Signal Distributions in Si

20. Event Statistics • 56 AGeV • Collision Events : 6352 • Central Events : 382 • Central Events (–25 < z < 15) : 103 • 130 AGeV • Collision Events : 12074 • Central Events : 724 • Central Events (–25 < z < 15) : 151

21. VTX Tracklets Two hit combinations that point to the vertex dh = h2 – h1 Good tracklets have dh<.1 Tracklets: Procedure • SPEC Tracklets • Two hit combinations that point to the vertex • dR =  (dh2 – df2) • Good tracklets have dR<.02

22. Tracklets: Result Peaked at ~0 so geometry is well understood Width agrees with simulation

23. Measuring dN/dh with tracklets • Number of reconstructed tracklets is proportional to dN/dh | |h|<1 • To reconstruct tracklets • Reconstruct vertex • Define tracklets based on the vertex and hits in the front planes of SPEC and VTX • Redundancy essentially eliminates feed-down, secondaries, random noise hits • To determine a • Run the same algorithm through the MC • Folds in detector response and acceptance

24. Uncorrected dN/dh • Final result extracted by integrating over Z

25. Derivation of dN/dh • Extract a(Z) from correlation of • Primaries in –1 < h < 1 • Measured number of tracklets 5<z<10 Number of Tracklets VTX SPEC dN/dh

26. Final Results for dN/dh

27. Comparisons with pp Submitted for publication

28. Towards a Stranger Future Strengths of Phobos • High event capability to give a large data sample • Minimum bias trigger with multiple ways to select centrality and vertex location • Numerous redundant quality checks to differentiate interesting rare events from rare combinations of background, pile-up, etc • Broad, segmented multiplicity detector • Almost complete charged particle coverage • Global and local particle density fluctuations • Two-arm spectrometer with low p^cutoff • Sensitive to physics of large volumes • Particle spectra, ratios, and correlations • May be possible to detectfnear p^~ 0

29. A Beginning, not a Conclusion • RHIC runs !!!! • So does ! • Now the real fun begins...