# Presentation: 529549

## Charged-particle dN/d from PHOBOS

- A. H. Wuosmaa
- (Argonne National Laboratory)
- for the
- PHOBOS Collaboration

- Quark Matter 2001

## The PHOBOS Collaboration

_**ARGONNE NATIONAL LABORATORY**_
**Birger Back, Nigel George, Alan Wuosmaa**
_**BROOKHAVEN NATIONAL LABORATORY**_
**Mark Baker, Donald Barton, Alan Carroll, Stephen Gushue, George Heintzelman, Robert Pak, Louis Remsberg,  Peter Steinberg, Andrei Sukhanov**
_**INSTITUTE OF NUCLEAR PHYSICS, KRAKOW**_
**Andrzej Budzanowski, Roman Holynski,, Jerzy Michalowski, Andrzej Olszewski, Pawel Sawicki , Marek Stodulski, Adam Trzupek, Barbara Wosiek, Krzysztof Wozniak**
_**MASSACHUSETTS INSTITUTE OF TECHNOLOGY**_
**Wit Busza***** ****,  Patrick Decowski, Kristjan Gulbrandsen, Conor Henderson, Jay Kane , Judith Katzy, Piotr Kulinich, Johannes Muelmenstaedt, Heinz Pernegger, Corey Reed, Christof Roland, Gunther Roland, Leslie Rosenberg, Pradeep Sarin, Stephen Steadman, George Stephans, Gerrit van Nieuwenhuizen,  Carla Vale, Robin Verdier, Bernard Wadsworth, Bolek Wyslouch**
_**NATIONAL CENTRAL UNIVERSITY, TAIWAN**_
**Willis Lin, JawLuen Tang**
_**UNIVERSITY OF ROCHESTER**_
**Erik Johnson, Josh Hamblen, Nazim Khan, Steven Manly,  Inkyu Park, Wojtek Skulski, R. Teng, Frank Wolfs**
_**UNIVERSITY OF ILLINOIS AT CHICAGO**_
**Russell Betts, Clive Halliwell, David Hofman, Burt Holzman, Wojtek Kucewicz, Don McLeod, Rachid Nouicer, Michael Reuter**
_**UNIVERSITY OF MARYLAND**_
**Richard Bindel, Edmundo Garcia-Solis, Alice Mignerey**
******* ****spokesperson**

## Why study dNch/d?

- dNch/d is sensitive to all aspects of charged-particle production in heavy-ion collisions:
- Interplay between hard and soft processes
- Effects of shadowing, jet quenching
- Reaction dynamics, re-scattering
- Full distribution reflects a time integral of particle production throughout the collision and total entropy production
- Lots of existing data for pp, pA, AB,AA
- How do RHIC data fit into this picture?

**Notes:**
To begin, I’d like to start by trying to answer, at least in part, the question of why it is we want to study dndeta. Actually, this conceptually simple observable is sensitive to all aspects of particle production in hi Collisions.  This includes things like the interplay between hard and soft processes, nuclear related effects such as shadowing or medium effects like jet quenching, dndeta is also sensitive to the dynamic evolution of the system and rescttering , and in effect represents a time integral over the then entire collision process.  so there is a lot of potentially complicated physics wrapped up in this one quantity.  Another reason to look at the pseudorapidity density is that there exist lots of data in the literature with which to compare, from a variety of systems from pp and pA to nucleus nucleus collisions, over a wide range of energies.  And the question, which I will probably not answer today but which will certainly be thought about in the near future is, how might new data from rhic fit into this global picture?

## Charged-particle Multiplicity in Au-Au at RHIC Energies

- Influence of various physical effects on dNch/din very central collisions

- (Wang & Gyulassy,
- Private communication)

**Notes:**
As an example of some different predictions of what we might find in auau at rhic, here are the results of two different models, the familiar HIJING model, and the saturation model from EK &S, with various additional nuclear effects turned on and off, such as parton shadowing, and jet quenching.  What seems interesting here is that these different processes have a substantial effect on the pseudorapidity distribution at mid-rapidity, but all the calculations seem to merge as we go out in eta.  This implies a rather different shape for dn/deta depending on which of these effects are included.  It’s possible that a measurement of dn/deta could help disentangle these different effects/processes.

## The PHOBOS Detector

- Octagon Multiplicity Detector

- Ring Multiplicity Detectors

- All multiplicity detectors
- are silicon pad sensors

**Notes:**
So now I’d like to turn to our measurement, which was carried out using 
some small pieces of the phobos spectrometer.  Here is a drawing of PHOBOS,
which you’ve already seen this morning, as it was during the year 2000 physics
run.  I’m not going to say anything at all about most of the experiment, I just 
want to focus on the multiplicity detectors which were used in our dndeta 
measurement.  And these detectors are indicated here - they are the octagon
multiplicity barrel, a 1m long silicon pad sensor array situated in the 
mid-rapidity region, and six multiplicity rings, which are silicon pad sensor 
arrays in the form of rings around the beamline as shown, sitting 1, 2, and about
5 m away from the central interaction point.

## Coverage of multiplicity detectors in 

**

- Schematic Multiplicity Detector

** coverage for vertex at Z=0.

**Notes:**
Here is a schematic view of the multiplicity array, just to illustrate our 
pseudorapidity coverage.  The detector was designed so that we would achieve
complete coverage all the way out to +- 5.5 units of pseudorapidity.  At the 
bottom I’ve just sketched how the coverage of the different parts of the 
multiplicity array fit together.  The colors of course correspond to those in the
schematic drawing above.  I should also point out that we’re sitting well into 
the fragmentation region; at root s of 130 GeV the beam rapidity is about 4.8.

## PHOBOS  Multiplicity detectors

- Octagon

- 1 of 6 Rings

**Notes:**
And just because I think that it is nice to show at least one picture of your 
detector to convince people that you aren’t just making it all up, here are 
photographs of some of the elements of the multiplicity array.  On the left is the
octagon, with the vertex detectors on the top and bottom.  You see the silicon,
and also some holes which are there so that silicon wafers and pieces of 
aluminum rib do not impinge on the acceptance of our tracking spectrometer.
On the right is on of the six ring detectors surrounding the beam pipe, and 
I don’t have much more to say about it except that its pretty.  For more details
about the experiment you should attend Robert Pak’s talk on Xxday.

## What I will not discuss:

- Event Selection
- Vertex Determination
- Centrality Determination
- See Talks by:
-  R. Pak, Tuesday 3:20
- J. Katzy, Wednesday 4:40
- And see Poster by:
- P. Decowski

**Notes:**
Before I go on I want to warn you about what I will not say in my talk:  I won’t
talk at all about the selection of events, the location of the collision vertex, or
the determination of the collision centrality.  These are topics that will be 
covered in depth by R. Pak, J. Katzy, and others, and if you are interested you
should go hear them speak.

## Count hits binned in , centrality (b)  
Calculate acceptance A(ZVTX) for that event
Find the occupancy per hit pad O(,b)
Fold in a background correction factor fB(,b)

- Calculate acceptance ***A***(*Z**VTX*)* *for that event
- Find the occupancy per hit pad ***O*****(***********,b*****)**
- Fold in a background correction factor ***f******B***(**,*b*)

- dNch

- d**

**=**

- hits

*O*(**,*b*) ×*f**B*(**,*b*)

*A*(*Z**VTX*)

- 

- 

***E deposition*
*in multiplicity*
*detectors for 1 event.*

**Notes:**
The first thing I want to describe is the method that we use to turn raw data into dndeta. We start with an event such as this - what is shown here is an event display for the multiplicity detectors, in the middle the octagon and on the ends the rings - and the square boxes represent the sensors in the array.  The colored blots represent pads that are hit in this event and the color tells you how much energy was deposited by the particle traverssing that pad.  First, for each event we count up the hits in the different bins of eta, of course separating events from different impact parameters or centralities, which I’ll refer to through most of the discussion by the symbol “b”.  Then, since we have some holes in our detector, how those holes affect the acceptance depends on where the collision took place, so we have to calculate the acceptance for each value of the vertex position and apply it event by event.  After that, we must take into account the fact that, esp.  For very central events, there are a lot of particles, and since the pads in our detector are not infinitely small, sometimes we have more than one particle going through at a time.  This effect is a function of both eta and centrality. Finally, un fortunately not all the particles are primary ones produced in the collision, and in fact some come from away from the vertex.  To cure this we have one more background correction that we must apply before we have the final dndeeta.  

## “Measuring” the occupancy

- “Measuring” the occupancy

*N*=number of tracks/pad
**=mean number of tracks/pad

- The numbers of empty, and occupied, pads
- determine the occupancy as a function of **,*b*

- Method: Assume Poisson
- statistics

- Ntracks/hit pad

- 

- 0-3%

- 50-55%

- Octagon

- Rings

- (central)

- (peripheral)

**Notes:**
Fortunately, we can actually measure the occupancy of our detector, and do it in a couple of  independent ways.  The first method involves studying the spectra of  energy loss spectra recorded by our silicon detectors.  Here is one such spectrum, and one can clearly discern the peak corresponding to mips.  In addition, you also see evidence of a second peak coming from two mips going through and so on.  By fitting a very large number of these spectra, one can map out the relative yields of 1,2 & 3 particles as a function of eta and b, and that gives you the  occupancy - as shown over hear on the right.  The different colors correspond to the occupancy measured in the octagon and rings, and this is for very central collisions. Here, the occupancy at mid-rapidity is about 1.6 part/ hit
pad.
Another way to estimate the occupancy is to assume that particles come out at random (probably not entirely true) and that the probability of some number of particles N hitting a pad is determined by poisson statistics.  If that is true, then we can determine the occupancy by simply observing how many pads are  occupied, and how many are not - that determines the quantity mu which is the mean occupancy.  Over here is the occupancy correction we get for the same centrality bin as above, and they are in reasonable agreement.  Any difference  contributes to our estimate of the systematic uncertainty in our measurement.

## Discriminating background with E 

- E (“MIP”)

- Data

- Monte Carlo

- Si

- E vs. ** in the Octagon

- From vertex

- Not from vertex

**

**

**Notes:**
The next thing we have to do is contend with background.  In the octagon, this turns out to be not such a big deal, because we can get rid of it by noticing that the characteristics of secondary particles, ie their energy loss is very different from those of particles that come from the vertex.  The reason is that esp. at the ends of the octagon, where we expect the most background as we have a thick beam pipe and some magnet steel, particles from the vertex go through a lot of silicon and deposit more energy.  You can see this by looking at the plot here which shows energy loss vs eta.  We have a very clear “smiley face” picture from primary particles from the vertex. Particles not from the vertex have different trajectories and go through less silicon, and deposit less energy, so we eliminate a large fraction of the background just by cutting on de. On the right is the same plot only with monte-carlo “data” showing exactly the same features.  I’ve also drawn the decut.  In fact this deals with a very large fraction of the secondary particles produced both in the beam pipe and the other parts of the experiment near the octagon.

## Estimating remaining backgrounds

- MC, Occupancy corrected

- MC “truth”

- 

- 

- Compare PHOBOS Monte Carlo “data” analyzed using
- occupancy corrections to “truth” -  the difference gives corrections  for remaining background.

*f*B(,*b*)

*f*B=MCTruth/MCOcc

- dNch/d**

- 200

- 400

- 600

## dNch/d for different centrality bins

- 45-55%

- 35-45%

- 25-35%

- 15-25%

- 6-15%

- 0-6%

- 

- 

- 

- dNch/d**

- dNch/d**

- dNch/d** for different centrality bins

- Statistical Unc. only

- PHOBOS Prelim.

**Notes:**
So now lets look at the data.  First of all let me show some distributions for a selected set of centrality bins, here described by the fraction of the cross section they correspond to. The colors are there just to show you that the data from these disparate pieces of the experiment actually match up together quite nicely.  And just looking at these distributions you can start to get a sense of  what’s happening as the data evolve with centrality.  For instance, its clear that for the most central collisions the data in the fragmentation region are relatively lower to those at mid rapidity than for more peripheral collisions.  But lets look at these things in a bit more detail.

## Centrality Dependence of Nch(||<5.4)

- Centrality Dependence of Nch(|**|<5.4)

- Npart

- ±10% Systematic Uncertainty

- HIJING

- Nch(||<5.4)

**Notes:**
Oh yes, before we go on to looking at features of the distributions, let me first
show you the centrality dependence of the total charged particle multiplicity,
here measured within our acceptance between +- 5.5 units of rapidity.  Here the blue data points represent our data, and the red points/line is the 
prediction of HIJING.  We see that the the data are a bit higher, with a maximum
of about 4200 charged particles between +-5.5.  

## Slide 15

## Shapes of dNch/d for different Npart

- Shapes of dNch/d for different Npart

**

**

- dNch/d

- (dNch/d)/(1⁄2Npart)

- dNch/d

- Data

- HIJING

- HIJING

- (dNch/d)/(1⁄2Npart)

- Systematic error ±(10%-20%)

- 354

- 216

- 102

- Mean Npart

- %

- 0-3

- 15-20

- 35-40

- Data

**Notes:**
To see how these data evolve with centrality, on the left we’ve plotted the
multiplicity distributions for three different centrality bins, here expressed as
numbers of participants.  On the top are the data unscaled, and on the bottom the
data are scaled by the number of participants. The corresponding results from
HIJING appear on the right.  We observe some rather striking behavior.  For
instance, whereas on the right, the scaled HIJING data all more or less converge
to the same values, we see that the shapes of the data are quite different. In 
particular, at large rapidity, for the most central collisions the scaled data are 
actually below there more peripheral counterparts – there is an apparent cross
over around eta=4 or so.  This could indicate some extra stuff coming from 
spectator matter, or possibly the influence of hadronic rescattering.  Also, just
in general the measured distributions are markedly wider than those predicted by
HIJING.  This could reflect a number of different effects, but we’d rather not
speculate about it for now.

## Centrality dependence of dNch/d|

- Centrality dependence of dNch/d**|

- Npart

- (dNch/d)/(1⁄2Npart)

- ||

- <1

- 5-5.4

- 4-4.4

- 3-3.4

- 2-2.4

- Symbols:

- Solid lines:  HIJING

- Errors are systematic

## Summary

- First multiplicity distributions over 4 now available at                                for wide range of impact parameters
- Nch(||<5.4)=4100±410 for the 3% most central collisions
- Distributions are somewhat wider than predicted by some models
- (dNch/d/(1⁄2Npart) in fragmentation region drops by ~1⁄2 from Npart=100 to 350
- Outlook:  coming analyses: EbyE, d2N/dd(I. Park, Wed. 5:55)

## We can’t wait for           200 GeV! 

- am anxiously looking forward to the main
- course”  -N. Khrushchev

- Or -

- We can’t wait for           200 GeV!

## Backup slides to follow:

## Data and Monte Carlo

- Comparison of DE measured
- in the Octagon at ||~3.0, with
- spectrum predicted by  Monte-Carlo:
- Conclusion: MC is a good predictor of background.

- (dNch/d)/(1⁄2Npart)

- 

## Occupancy reality check

- Can determine from DE relative yields of events with
- 1,2,3... hits/pad: gives independent measurement of Occupancy

- Good: fewer potentially unjustified assumptions
- Bad:  Fitting procedure somewhat unstable, occasional large errors

- Very Good:  We get the same answer in the end!!

## Comparison of all methods at mid rapidity

**MULT ARRAY**

## pp data from ISR

**

*pp* data from ISR

- W. Thome et al.,
- Nucl. Phys. **B129**
- (1977) 365.

**Notes:**
To start to put things in perspective, let me start by showing some old but very nice pp scattering data from the ISR. Here are multiplicity distribution data taken at 5 different values of root s as shown across the figure,
and also as a function of the total observed multiplicity, shown from top to bottom.  Its interesting to see how
these shapes evolve as the energy, and the “violence” of the collisions increase.  We observe the characteristic
rapidity plateau around mid rapidity, and with the increasing number of produced particles we see the distributions
narrow considerably in eta.  This phenomenon has been associated with the production of jets and many particles with large transverse energy.  These kinds of features might be what we could look for in the production of charged
particles from AuAu collisions at RHIC.