# Presentation: 479637

## Multiplicity as a measure of Centrality in    			

- Richard S Hollis
- University of Illinois at Chicago

##             Collaboration (May 2004)

- Birger Back, Mark Baker, Maarten Ballintijn, Donald Barton, Russell Betts, Abigail Bickley,
- Richard Bindel, Wit Busza (Spokesperson), Alan Carroll, Zhengwei Chai, Patrick Decowski,
- Edmundo García, Tomasz Gburek, Nigel George, Kristjan Gulbrandsen, Clive Halliwell,
- Joshua Hamblen, Adam Harrington, Michael Hauer, Conor Henderson, David Hofman, Richard Hollis,
- Roman Hołyński, Burt Holzman, Aneta Iordanova, 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, Helen Seals, Iouri Sedykh, Wojtek Skulski,
- Chadd Smith, Maciej Stankiewicz, 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
- 	**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**

## Outline

- Considerations – Centrality in 200GeV *Au+Au*
- Event Selection
- Detector Efficiency
- Choose Data η region to measure centrality
- Event Generator Simulations – Npart
- Other Collision Systems
*d+Au* 200 GeV
*Au+Au* 19.6 GeV

## The                   Detector

- Paddle Counters
- and ZDCs

- ZDC

- ZDC
- (hidden)

**The                   Detector**

- Ring Counters

- Octagon and
- Spectrometer

## A clean event selection ensures consistency of good events

Only use data within ±4ns (~±60cm) from Paddles
Red points → outside this cut the acceptance changes

Further restrictions requires ZDC signal

- A clean event selection ensures consistency of good events
- Only use data within ±4ns (~±60cm) from Paddles
- Red points → outside this cut the acceptance changes
- Further restrictions requires ZDC signal

- Beam-gas collision peaks

- Considerations: Event Selection

- 200GeV *Au+Au* Collisions

**Schematic Diagram of Paddles**

## Considerations: Event Selection

- A clean event selection ensure consistency of good events
- Only use data within ±4ns (~±60cm) from Paddles
- Red points → outside this cut the acceptance changes
- Only use Silicon vertex range of ±10cm for physics measurements (blue)

## Considerations: Detector Efficiency

- Trigger system incurs small bias for peripheral events
- Have to account for the missing cross-section

- Have ***two*** main trigger types (in *Au+Au*)
- One or more hit in each paddle array
- Estimated to have 97% efficiency
- More than 2 hits in each paddle array
- Estimated to have 88% efficiency

## Considerations: Detector Efficiency

- Trigger system incurs small bias for peripheral events
- Have to account for the missing cross-section
- Estimate the efficiency
- Using Data and MC simulations

-  Unbiased Hijing sample
-  97% efficient trigger (red) Data
-  88% efficient trigger (blue) Data

- Number of hit paddle segments

- Similar plateau
- observed in data

## Considerations: Data Centrality Region

- Paddles are located in 3.2 < |η| < 4.5
- Monotonic anti-correlation with neutral spectators
- Region appears to be usable

- 200 GeV *Au+Au*
- data for 0-25% cross-section

## Centrality Determination

- Summary of Information
- Have clean event selection
- Estimated the trigger efficiency
- Centrality determined from Paddles (3.2<|η|<4.5)
- Observed spectators are monotonic with that signal
- Need one more piece
- Does MC predict monotonicity between Npart and Paddle signal?

## Centrality Determination

- Nicely correlated
- Now have all the information needed
- Hard work is done
- Divide the data into cross-section bins
- Use MC to estimate <Npart> for each bin

**Only use data where (Si) vertexing finding efficiency is 100%**
**(top 50% of cross-section)**

## Centrality Complete

- 200 GeV *Au+Au* is well established
- Now investigate the centrality of
*d+Au* collisions at 200 GeV

## Centrality at 200GeV d+Au

- Same Considerations
- Details of analysis is different
- Event Selection
- Clean-up by requiring a valid silicon vertex
- Very Low multiplicity Events
- Paddle timing smeared
- Not always neutron in both ZDCs
- Efficiency
- Used a shape matching algorithm between Data and Simulations (HIJING or AMPT)
- Efficiency includes Trigger and Vertex finding efficiency
- Estimated to be 82.5%

**Hijing + GEANT**
**Data**

- Shapes agree reasonably in High multiplicity region

- Data inefficient for peripheral events

**EOct is the summed charge deposited in the Octagon detector**

## Unique PHOBOS η coverage
Many regions to pick from
Not just the ‘paddles’

All regions were used
same basic algorithm
Sum the charge deposited in these regions (from Silicon)

- Many regions to pick from
- Not just the ‘paddles’
- All regions were used
- same basic algorithm
- Sum the charge deposited in these regions (from Silicon)

- d+Au Data Centrality Regions

- EOct

- ERing

- ETot

- EdHem

- EAuHem

## Unique PHOBOS η coverage
Many regions to pick from
Not just the ‘paddles’

All regions were used
same basic algorithm
Sum the charge deposited in these regions (from Silicon)

- Unique PHOBOS η coverage
- Many regions to pick from
- Not just the ‘paddles’
- All regions were used
- same basic algorithm
- Sum the charge deposited in these regions (from Silicon)

- d+Au Data Centrality Regions

## d+Au Data Centrality Biases

- Two types of potential biases
- Auto-correlation biases
- Measurement of centrality interferes with physics measurement
- Causes a change in shape of dN/dη distribution
- Trigger-Biases
- Due to inefficiency for peripheral collisions
- Changes <Npart> of a bin

## Auto-Correlation Biases

- Mid-rapidity Centrality, EOct
- Shown for 3 cross-section bins
- Form a *Truth* distribution
- Divide the Npart distribution into bins with the same <Npart> as EOct
- Repeat analysis
- Can see a clear bias developing due to the centrality determination

- AMPT+GEANT Simulations

**EOct**

**Npart**

## Auto-Correlation Biases

- Mid-rapidity Centrality, ERing
- Shown for 3 cross-section bins
- Bias is smaller

- AMPT+GEANT Simulations

**ERing**

**Npart**

## Centrality at 200GeV d+Au

- With these two things
- can make the centrality dependence
- Can now integrate these distributions for the total charge
- Scale by Nchpp
- Results consistent with expectations from low energy data

## Centrality at 200GeV d+Au

- With these two things
- can make the centrality dependence
- Can now integrate these distributions for the total charge
- Scale by Nchpp
- Results consistent with expectations from low energy data

## d+Au Centrality Complete

- Final Example:
*Au+Au* collisions at 19.6 GeV

## Centrality at 19.6GeV Au+Au

- Hit number of Paddles efficiency method
- Works at 62.4 GeV
- Does not work at 19.6 GeV
- Too low multiplicity
- No plateau observed
- Multiplicity in paddle’s region too low

- See the same plateau

## Centrality at 19.6GeV Au+Au

- Use summed charge in octagon
- EOct as in *d+Au*
- Could this introduce a bias?
- Make new Centrality measures to check this

## Centrality at 200GeV Au+Au

- Measure same results for
- midrapidity methods (a)
- away from midrapidity (b)
- For midrapidity yields for the top 50% of the cross-section
- Aneta Iordanova will talk about this measurement in the next talk

- 200GeV from (a)

- 200GeV from (b)

## Conclusions

- We have established methods for centrality determination
- From 19.6 to 200 GeV *Au+Au*
- From *d+Au* to *Au+Au* collision systems
- Have an array of complementary techniques for centrality cross-checks
- Techniques used introduce little or no bias onto the physics measurements
- Any biases introduced can be estimated and corrected for from MC studies