arXiv:hep-ex/9710015v1 17 Oct 1997
Search For Disoriented Chiral Condensates In
158 AGeV Pb+Pb Collisions
M.M.Aggarwala, A.Agnihotrib, Z.Ahammedc, A.L.S.Angelisd,
V.Antonenkoe, V.Arefievf, V.Astakhovf, V.Avdeitchikovf,
T.C.Awesg, P.V.K.S.Babah, S.K.Badyalh, A.Baldinef,
L.Barabachf, C.Barlagi, S.Bathei, B.Batiouniaf, T.Bernierj,
K.B.Bhallab, V.S.Bhatiaa, C.Blumei, R.Bockk, E.-M.Bohnei,
D.Bucheri, A.Buijsℓ, E.-J.Buisℓ, H.B¨ uschingi, L.Carlenm,
V.Chalyshevf, S.Chattopadhyayc, R.Cherbatcheve, T.Chujon,
A.Clausseni, A.C.Dasc, M.P.Decowskir, V.Djordjadzef,
P.Donnid, I.Doubovike, M.R.Dutta Majumdarc,
K.El Chenawim, S.Eliseevo, K.Enosawan, P.Fokad, S.Fokine,
V.Frolovf, M.S.Gantic, S.Garpmanm, O.Gavrishchukf,
F.J.M.Geurtsℓ, T.K.Ghoshp, R.Glasowi, S.K.Guptab,
B.Guskovf, H.A.Gustafssonm, H.H.Gutbrodj, R.Higuchin,
I.Hrivnacovao, M.Ippolitove, H.Kalechofskyd, R.Kamermansℓ,
K.-H.Kamperti, K.Karadjeve, K.Karpioq, S.Katon, S.Keesi,
H.Kimg, B.W.Kolbk, I.Kosarevf, I.Koutcheryaeve, A.Kuglero,
P.Kulinichr, V.Kumarb, M.Kuratan, K.Kuritan, N.Kuzminf,
I.Langbeink, A.Lebedeve, Y.Y.Leek, H.L¨ ohnerp, L.Luquinj,
D.P.Mahapatras, V.Mankoe, M.Martind, A.Maximovf,
R.Mehdiyevf, G.Mgebrichvilie, Y.Miaken, D.Mikhalevf,
G.C.Mishras, Y.Miyamoton, D.Morrisont,
D.S.Mukhopadhyayc, V.Myalkovskif, H.Naefd, B.K.Nandis,
S.K.Nayakj, T.K.Nayakc, S.Neumaierk, A.Nianinee,
V.Nikitinef, S.Nikolaeve, S.Nishimuran, P.Nomokonovf,
J.Nystrandm, F.E.Obenshaint, A.Oskarssonm, I.Otterlundm,
M.Pachro, A.Parfenovf, S.Pavlioukf, T.Peitzmanni,
V.Petraceko, F.Plasilg, M.L.Purschkek, B.Raevenℓ, J.Rako,
S.Raniwalab, V.S.Ramamurthys, N.K.Raoh, F.Retierej,
K.Reygersi, G.Rolandr, L.Rosseletd, I.Roufanovf, C.Royj,
J.M. Rubiod, H.Sakon, S.S.Sambyalh, R.Santoi, S.Saton,
Preprint submitted to Elsevier Preprint5 February 2008
H.Schlaghecki, H.-R.Schmidtk, G.Shabratovaf, I.Sibiriake,
T.Siemiarczukq, B.C.Sinhac, N.Slavinef, K.S¨ oderstr¨ omm,
N.Solomeyd, S.P.Sørensent, P.Stankusg, G.Stefanekq,
P.Steinbergr, E.Stenlundm, D.St¨ ukeni, M.Sumberao,
T.Svenssonm, M.D.Trivedic, A.Tsvetkove, C.Twenh¨ ofelℓ,
L.Tykarskiq, J.Urbahnk, N.v.Eijndhovenℓ, W.H.v.Heeringenℓ,
G.J.v.Nieuwenhuizenr, A.Vinogradove, Y.P.Viyogic,
A.Vodopianovf, S.V¨ or¨ osd, M.A.Vosℓ, B.Wys? louchr, K.Yagin,
aUniversity of Panjab, Chandigarh 160014, India
bUniversity of Rajasthan, Jaipur 302004, Rajasthan, India
cVariable Energy Cyclotron Centre, Calcutta 700 064, India
dUniversity of Geneva, CH-1211 Geneva 4,Switzerland
eRRC (Kurchatov), RU-123182 Moscow, Russia
fJoint Institute for Nuclear Research, RU-141980 Dubna, Russia
gOak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6372, USA
hUniversity of Jammu, Jammu 180001, India
iUniversity of M¨ unster, D-48149 M¨ unster, Germany
jSUBATECH, Ecole des Mines, Nantes, France
kGesellschaft f¨ ur Schwerionenforschung (GSI), D-64220 Darmstadt, Germany
ℓUniversiteit Utrecht/NIKHEF, NL-3508 TA Utrecht, The Netherlands
mUniversity of Lund, SE-221 00 Lund, Sweden
nUniversity of Tsukuba, Ibaraki 305, Japan
oNuclear Physics Institute, CZ-250 68 Rez, Czech Rep.
pKVI, University of Groningen, NL-9747 AA Groningen, The Netherlands
qInstitute for Nuclear Studies, 00-681 Warsaw, Poland
rMIT Cambridge, MA 02139, USA
sInstitute of Physics, 751-005 Bhubaneswar, India
tUniversity of Tennessee, Knoxville, Tennessee 37966, USA
The restoration of chiral symmetry and its subsequent breaking through a phase
transition has been predicted to create regions of Disoriented Chiral Condensates
(DCC). This phenomenon has been predicted to cause anomalous fluctuations in
the relative production of charged and neutral pions in high-energy hadronic and
nuclear collisions. The WA98 experiment has been used to measure charged and
photon multiplicities in the central region of 158 AGeV Pb+Pb collisions at the
CERN SPS. In a sample of 212646 events, no clear DCC signal can be distinguished.
Using a simple DCC model, we have set a 90% C.L. upper limit on the maximum
DCC production allowed by the data.
The approximate chiral symmetry of the QCD vacuum is believed to be spon-
taneously broken in nature by the formation of an isoscalar quark conden-
sate. Disoriented Chiral Condensates (DCC) may form in large, hot regions
of hadronic matter where this symmetry has been briefly restored . A DCC
has an equal probability to be in any state related to the normal vacuum by
a chiral rotation. By projecting the space of these available states onto a ba-
sis of definite isospin, it has been found that the charge distribution of pions
emitted from a DCC has a characteristic form :
where f is the neutral fraction,
Nπo + Nπ+ + Nπ−. (2)
This allows the possibility of hadronic interactions with anomalous fluctua-
tions between charged pions and neutral pions, as seen through their two-
photon decay channel.
The phenomenology of DCCs was first introduced in the context of hadronic
collisions by Bjorken et al, whose “Baked Alaska” model  postulated that a
hot shell, expanding at the speed of light, could shield the cool interior from
the influence of the normal vacuum outside, allowing a large region of DCC to
form. Rajagopal and Wilczek [4,5] studied the production of DCCs in nuclear
collisions by studying the chiral phase transition in QCD, via its similarity to
the O(4) Heisenberg magnet . Through numerical simulations, they found
that as the system rapidly expands and cools through the phase transition,
the equations of motion induce a non-equilibrium relaxation of the chiral fields
which amplifies the production of soft pion modes in a well-defined chiral direc-
tion. This effectively creates clusters of low-pT pions, with the cluster charge
distribution following equation (1). It should be noted that, in both studies,
the strongest influence on the final state composition is the symmetry itself
rather than the exact physics scenario studied. Further work confirmed these
initial results, even after accounting for quantum fluctuations, and proposed
other mechanisms which might allow for large, long-lived DCCs.
By allowing the possibility of events with almost no electromagnetic energy,
DCCs are an attractive hypothesis to explain the “Centauro” events seen in
cosmic rays . These events have already motivated searches for unusual
charge fluctuations at the SppS (by UA1  and UA5 ) and at the Teva-
tron (by Minimax  and CDF ). And yet, there have been no systematic
studies utilizing the simultaneous measurement of charged and neutral mul-
tiplicities in heavy ion collisions at any energy. It has been argued  that
heavy ion collisions at SPS energies, the highest currently available, might
create the large volumes which favor the development of long-wavelength os-
cillations within the reaction zone. It is true that large baryon number in the
central region complicates theoretical calculations and may obscure the initial
signal via the rescattering of secondaries. It is also possible that the low-energy
observation that most pions are produced resonantly via the ρ and ω channels
leaves few “direct” pions which may be influenced by a DCC. We must keep in
mind, however, that these are extrapolations from lower energies. Their cumu-
lative effect is uncertain, especially at higher energies where the formation of a
quark-gluon plasma would render previous measurements inapplicable. In any
case, in the absence of any substantial experimental evidence for or against
DCCs in heavy ion collisions at SPS energies, and a great deal of theoretical
evidence in their favor, it is imperative to simultaneously measure charged and
neutral particles at the SPS and analyze their fluctuations to perhaps isolate
a DCC signal. Observing such a signal might be an indication of the chiral
phase transition in hot nuclear matter.
2 Experimental setup
The WA98 experiment  is a general-purpose, large-acceptance photon and
hadron spectrometer with the ability to measure several different global ob-
servables event-by-event. For this search, we use a subset of the full apparatus,
shown schematically in Figure 1. We measure charged particles with a Silicon
Pad Multiplicity Detector (SPMD) and photons with a Photon Multiplicity
Detector (PMD). Using these, we are able to count charged particles and pho-
tons in the central pseudorapidity region on an event-by-event basis. For a
determination of the centrality of each collision, we use the transverse energy
(ET) measured in the Midrapidity Calorimeter (MIRAC ). For removal of
background events, we also use the Zero-Degree Calorimeter (ZDC) and the
Plastic Ball detector .
2.1Charged particle multiplicity
We count charged particles using a circular Silicon Pad Multiplicity Detector
(SPMD)  located 32.8 cm from the target covering 2.35 < η < 3.75, the
central rapidity region at SPS energies (where ηCMS= 2.9), and full azimuth.
The detector consists of four overlapping quadrants, each fabricated from a
single 300 µm thick silicon wafer. The active area of each quadrant is divided
into 1012 pads forming 46 azimuthal wedges and 22 radial bins with a pad
size increasing with radius to provide uniform pseudorapidity coverage. The
efficiency of detecting a charged particle in the active area has been determined
in a test beam to be better than 99%. Conversely, the detector is transparent
to high energy photons, since only about 0.2% are expected to interact in the
silicon. During the data taking, 95% of the pads worked properly and are used
in this analysis.
In a central ion-ion collision, the occupancy can be as high as 20%, imply-
ing that ≈ 20% of the pads contain two or more hits. An unbiased way to
estimate the total number of charged particles in a given event under such
conditions is to use the sum of the energy deposited in pads exceeding 1/2 of
the most probable energy loss divided by the mean energy loss per particle as
determined in low-multiplicity events:
Because of the fluctuations in the energy loss, described by a Landau distribu-
tion, the uncertainty on Nchcan be estimated to be ∆N/N = 60%/√N. For
typical central events with a multiplicity of ≈ 600, this gives an uncertainty
of about 2%. To check the overall scale, we compare the results with the mul-
tiplicity obtained by assuming that the particles are distributed uniformly so
the multi-hit probability is given by Poisson statistics. A simple calculation
pads, and Nhits is the total number of hit pads. Using this as a check, we
estimate the systematic error on Nch, due to uncertainties in the gains and
backgrounds, to be about 4%.
ch= −Npadslog(1 − Nhits/Npads), where Npadsis the total number of
2.2 Photon multiplicity
We count photons in the preshower Photon Multiplicity Detector (PMD) sit-
uated 21.5 m from the target, covering the region 2.8 < η < 4.4. The photons
impinging on the detector are converted in 3.34 X0thick lead and iron and
the secondaries are detected in 3mm-thick square plastic scintillator pads of
varying sizes (15mm, 20mm and 23mm). A matrix of 50 × 38 pads is placed in
one light-tight box module and read out individually via wavelength shifting
optical fibers coupled to an image intensifier and CCD camera system similar
to that described in . The modules with smaller pads were mounted in the
forward angle region to minimize cluster overlap at large multiplicities and
to provide reasonably uniform occupancy. Out of a total of 28 box modules
implemented in the PMD, the data presented here correspond to 19 box mod-
ules having 35524 pads. The average occupancy for the part of the detector
considered in the present case is around 15% for central events.
The principle of photon identification makes use of the fact that photons are
more likely to shower in the lead converter and produce a large signal in the
scintillator pads, while non-showering hadrons will produce a signal corre-
sponding to a single minimum ionizing particle (MIP). Signals from several
neighbouring pads are combined to form clusters and those with energy depo-
sition larger than that corresponding to 3 MIPs are considered to be ”γ-like”
clusters. This selection gives an average photon counting efficiency of about
70% which is almost uniform over the range of centrality and pseudorapidity
considered. It also creates an effective lower pT cutoff of 30 MeV/c, at which
point the efficiency falls below 35%. About 15% of the produced hadrons im-
pinging on the PMD interact in the converter, generating secondaries which
also deposit large energy on the detector. This contamination constitutes a
background to photon counting. In order to minimize effects due to variations
in the angular distributions of charged particles, we only use data with the
Goliath magnet turned off.
The photon counting efficiency, hadron contamination and the associated er-
rors are derived using test beam data and GEANT simulation using a method
similar to the ones described in [16,17]. The level of hadron contamination
in the PMD was verified by comparing the azimuthal distribution of hits for
magnet-on and magnet-off data. The azimuthal distribution of charged
tracks becomes very non-uniform in the presence of the magnetic field, the
amount of non-uniformity indicating the magnitude of the hadron contamina-
It should be emphasized that in this analysis, we do not correct the data using
these parameters. Instead we account for all of the detector effects by fully
simulating the conversion of particles in the detector, as described below.
2.3Data and Event Selection
In this analysis, we study reactions induced by a 158 AGeV Pb beam incident
upon a 213µm thick208Pb target. The fundamental “beam” trigger condition
consists of a signal in a gasˇCerenkov start counter  located 3.5 meters
upstream of the target and no coincident signal in a veto counter with a 3mm
circular hole located 2.7 meters upstream from the target. A beam trigger is
considered to be a minimum-bias interaction if the transverse energy sum in
the full MIRAC acceptance exceeds a lower threshold.
Pileup events are eliminated using a system of TDCs each started by a partic-
ular trigger counter and stopped by a second trigger. Using these, we remove
events where a second interaction occurred within a ±10µs window before
and after the recorded event. Still, our TDC system cannot distinguish two
events that arrive less than 50 ns apart. These are eliminated by requiring the
sum of energy deposited in the MIRAC (3.5 < η < 5.5) and ZDC (η > 6) to
be consistent with a single event. After applying these cuts, 70% of the data
3 General Features of Data and Comparison with VENUS 4.12
To describe the bulk of the data, we use the VENUS 4.12  event generator
with its default settings. To compare VENUS with our data, we propagate
the raw generator output through a full simulation of our experimental setup
using the GEANT 3.21  package from CERN. The simulation incorporates
the detector physics effects and folds them into the generated data, which is
then analyzed using the same code used for the raw experimental data. In the
rest of this paper, the term “VENUS” refers to the combination of VENUS
4.12 and the full GEANT 3.21 detector simulation, not to the raw generator
output, unless otherwise specified.
The SPMD simulation includes the effect of Landau fluctuations in the energy
loss of charged particles in the silicon and the pad geometry of the detector. In
addition to the secondaries from the ion-ion collision itself, the SPMD is also
sensitive to the δ-rays generated by the82+Pb ion passing through the lead
target. We can get a conservative estimate of the δ-ray multiplicity in physics
events by studying events that satisfy the conditions for a beam trigger but
not the interaction trigger. These “beam” events have a mean multiplicity
in the SPMD of 11.4±.5 and a width of 5.9±.3. The angular distribution is
consistent with a spatially uniform illumination of the detector surface. To
include these ion-induced δ-rays in the simulation, we sample the measured
charged multiplicity distribution for beam events and add it to the charged
particle multiplicity for each simulated event. We estimate the uncertainty
in the absolute scale of Nchfrom the simulation to be less than 3% and the
relative uncertainty between data and VENUS to be less than 2%.
The PMD simulation also incorporates the effects of additional fluctuations to
the energy loss arising due to the statistical nature of the scintillation process;
light transport through the wavelength shifting fibres and the image intensifier
chains; and imperfections in the electro-optical imaging. The widths due to
this extra fluctuation were obtained by a comparison of the GEANT and test
beam results using single pions and electrons at various energies. As all of
the readout chains were not used in the test beam experiment, a method of
detailed intercomparison of the various features of data and simulation was
used to obtain the gains of the individual readout units. We estimate the
uncertainty on the absolute multiplicity scale of simulated γ-like clusters, due
to uncertainties in various parameters of the simulation and data analysis, to
be 15%, and that the relative uncertainty between data and VENUS is 5%.
In Figures 2a and 2b we present the minimum-bias multiplicity distribution for
charged particles and γ-like clusters. For the DCC search, we will concentrate
on the 10% most central events, defined by a measured transverse energy of at
least 300 GeV in 3.5 < η < 5.5. These correspond roughly to the top 620 mb
of the Pb+Pb minimum bias cross section σmb=6200 mb. After all cuts are
applied, there are 212646 events in this sample, which we will refer to as the
“central” sample in the rest of this paper. The central data sample is shown by
closed circles in Figs. 2a and 2b and a comparison with VENUS events chosen
by identical cuts is shown by the histogram. The correlation between the
charged and neutral multiplicities is presented in Figure 3 with the minimum
bias distribution outlined, the central VENUS events hatched, and the central
data events shown as scattered points, each point corresponding to a single
The most distinctive feature of the scatter plot is the strong correlation be-
tween the charged and neutral multiplicities. A reasonable explanation of
this would be if most of the produced particles are pions with their charge
states partitioned binomially, as measured in pp experiments at similar ener-
gies . A binomial distribution leads to a correlation width σ(Nch− Nγ) ∝
Nch+ Nγ, which would explain the very tight correlation, since the relative
fluctuations are proportional to 1/
Nch+ Nγ. As this is seen in both data
and VENUS, we can study the contributions to the different multiplicities to
verify this hypothesis. In fact, about 80% of the charged particles produced in
VENUS are pions, the rest being protons and kaons. Moreover, about 85% of
produced photons come from πodecays. Thus, by simply counting the charged
particles and photons produced in a heavy ion collision, we have a reasonable
estimate of the number of charged and neutral pions created.
We verify the binomial nature of the charge fluctuations in VENUS by study-
ing its “binomiality”:
pch(1 − pch)Nπ
where Nπchand Nπ are number of charged pions and the total number of
pions for each event, and pch = Nπch/Nπ is the probability that a pion is
charged. For a pure binomial distribution, pch= 2/3 and B is Gaussian with
a mean at zero and an RMS of one. For VENUS without GEANT, we find an
RMS of approximately .95 for pions produced in the central rapidity region in
events with an impact parameter less than 6 fm. This is consistent with the
hypothesis that the correlation arises mainly from the binomial partition of
Nπ, the total pion multiplicity.
4 Event-by-Event Search for DCCs
DCCs should modify the binomial partitioning of Nπinto charged and neutral
pions. Events in which a DCC is produced (henceforth referred to as “DCC
events”) will show up as deviations from the binomial behavior and appear
as outliers with respect to the bulk of the data. We have already discussed
that the charged and neutral multiplicities are directly sensitive to the charged
and neutral pion multiplicities in each event. Thus, DCC events should appear
in the correlation of charged and neutral multiplicities, while the individual
distributions will be mainly unaffected.
4.1 Data Analysis
The strong correlation between charged and neutral multiplicities described
above suggests a more appropriate coordinate system with one axis being the
measured correlation axis and the other perpendicular to it. If all detected
particles were pions and the detectors were perfect and had identical pseudo-
rapidity acceptance, then the correlation axis would be a straight line. Instead,
we must account for the fact that at high multiplicities, the pseudorapidity
distributions tend to narrow, changing the relative acceptance of charged and
neutral particles due to the non-identical apertures of the SPMD and PMD.
Moreover, the large occupancies in the PMD lead to a slight saturation effect.
It is then useful to define a coordinate system consisting of a correlation axis
(Z) described by a second-order polynomial, and the perpendicular distance
(DZ) from it, which is defined to be positive for points below this Z axis.
These axes are shown superimposed on Figure 3 and the projection along the
Z-axis is shown in Figure 4a. The full projection along the DZ-axis is shown in
Figure 4b. To a very good approximation, the data are Gaussian distributed,
which is consistent with binomial partition. The VENUS results, shown by
the histogram, are also Gaussian, but with a slightly smaller width.
In both cases, σDZ, the standard deviation of a gaussian fit in the DZ di-
rection, increases with increasing Z. We have chosen to work with the scaled
variable SZ≡DZ/σDZin order to compare relative fluctuations at different
multiplicities. While binomial partition leads to fluctuations that grow as√N,
the data and VENUS follow a slightly different power law, due to the pres-
ence of contaminating particles, like nucleons kaons, and etas. A reasonable
parametrization of σDZfor Z > 200 has been found to be σDZ= C+Zβwhere
C = 7.5±.1 for the data, and C = 4.8±.1 for the simulated events, and β=.46.
The discrepancy between VENUS and the data can be seen more clearly by
measuring the width of the SZdistribution with the σDZin the denominator
taken from the simulation. The VENUS distribution is a gaussian of width
.998±.002 (fit error only) and the data is also gaussian, of width 1.13±.07
(error from relative scale uncertainties included).
4.2Model of DCC production
To estimate the effect of DCC production we have modified the VENUS events
to include characteristic fluctuations in the relative production of charged
and neutral pions. We assume that only a single domain of DCC is formed
in each central collision. A certain fraction ζ = NDCC/Nπ of the VENUS
pions is associated with this domain and a value of f is chosen randomly
according to the distribution shown in equation 1. Then the charges of the
pions are interchanged pairwise (π+π−or πoπo) until the charge distribution
matches the chosen value of f. This simulates a DCC accompanied by the
normal hadronic background in a way that conserves energy, momentum, and
charge. The SZdistribution for the ζ = 0%, 25% and 60% DCC hypotheses
are shown in Figure 5 with the data overlaid as closed circles. It is clear that
the distributions get wider as ζ is increased.
4.3 Upper Limit Calculation
As seen in the previous section, DCC events will show up as non-statistical
tails on the DZ axis. We see no such events in our data sample. Thus, we
are faced with the possibilities that single-domain DCCs are very rare, very
small, or both. To check which hypotheses are consistent with our data, we
determine upper limits on the frequency of DCC production as a function of
its size, as represented by ζ.
We have computed SZdistributions for several values of ζ, ranging from 15%
to 90%. To define an efficiency for detecting DCCs, we start from the obser-
vation that the distribution assuming the null hypothesis is Gaussian. With
our statistics, we expect few events farther than 5 to 6 σ from the mean. An
event containing a DCC, however, has an enhanced probability of being found
in this region. The cut |SZ| > Scutthen defines a two-dimensional region in
the scatter plot in which all events are considered to be “DCC candidates”.
Once the cut is set, the DCC efficiency is defined, for NMCVENUS events, as
ǫ(Scut,ζ) =N(|SZ| > Scut,ζ)
which is a function of both the DCC fraction and the cut position.
The background is determined by a Gaussian fit to the VENUS distribution,
in order to extrapolate beyond the Monte Carlo statistics. With the efficiency
and background determined, we calculate the Poisson upper limit NU.L.for a
90% confidence level, which is ≈2.3 if there are no measured events over the cut
and no background events are expected. These three numbers are combined
into an upper limit, for NDataevents, via the formula:
We have calculated limits for two scenarios. The first is based upon the con-
servative assumption that VENUS should describe the data perfectly in the
absence of a DCC signal. Under these assumptions, SZ= DZ/σDZas obtained
from VENUS (as it was in Figure 5) and Scut= 6., which is well away from
the data point with the largest SZ. The 90% C.L. limit is presented in Figure
6 as a solid line. The other scenario assumes that the difference between the
data and VENUS is due to detector effects and that the widths should be
the same. In this case, SZ= DZ/σDZ, with σDZtaken from the data, and we
choose a tighter cut Scut= 5. This limit is presented in the same figure as a
dashed line. The two limits are quite different at ζ =15% but get closer at ζ >
30%. In both cases, the uncertainty in the absolute comparisons between the
data and VENUS have not been included in the upper limit estimate.
Earlier studies estimated the DCC radius to be around R ≈ 3−4 fm. Coupled
with a vacuum energy density u given by the chiral effective potential to be
60-120 MeV/fm3, and an assumed Gaussian pT distribution of width ≈ 1/R,
an average DCC was thought to generate4
The SPMD sees all of the charged particles produced in the central rapidity
region, 80% of which are pions, letting us estimate the total number of centrally
produced pions in an average event to be about 720. Thus, we would expect a
DCC to be approximately ζ≈5−30%. Our analysis clearly rules out anything
larger than about 25% within the scope of the assumed model, but cannot say
much about anything smaller. However, if the pions tend to cluster in phase
space, there are methods that should be able to find DCC events, and these
are currently under study [24,25].
3πR3(u/mπ) ≈ 50 −230 pions .
A small and frequent DCC might also appear as a slightly enhanced width,
similar to what we observe in our data when compared to VENUS. However,
this enhancement could also result from uncertainties in the detector modelling
or the underlying physics model itself. For instance, theoretical uncertainties
might arise because no model has ever been used to study charge correlations
in heavy ion collisions. Rescattering phenomena, resonances, or Bose-Einstein
effects may have predictable effects on the expected binomial distribution.
These issues will be addressed in future studies.
We have used the WA98 apparatus to perform the first search for the produc-
tion of Disoriented Chiral Condensates in 158 AGeV Pb+Pb collisions. No
events with large charged-neutral fluctuations have been observed. By com-
paring the correlations of the charged and neutral multiplicity, measured on
an event-by-event basis, to a simple model incorporating a DCC signal into
VENUS 4.12 events, we have set a 90% CL upper limit on the frequency of
DCC production as a function of its size.
We wish to express our gratitude to the CERN accelerator division for excel-
lent performance of the SPS accelerator complex. We acknowledge with appre-
ciation the effort of all engineers, technicians and support staff who have par-
ticipated in the construction of the this experiment. This work was supported
jointly by the German BMBF and DFG, the U.S. DOE, the Swedish NFR, the
Dutch Stichting FOM, the Stiftung fuer Deutsch-Polnische Zusammenarbeit,
the Grant Agency of the Czech Republic under contract No. 202/95/0217, the
Department of Atomic Energy, the Department of Science and Technology,
the Council of Scientific and Industrial Research and the University Grants
Commission of the Government of India, the Indo-FRG Exchange Programme,
the PPE division of CERN, the Swiss National Fund, the International Sci-
ence Foundation under Contract N8Y000, the INTAS under Contract INTAS-
93-2773, ORISE, Research-in-Aid for Scientific Research (Specially Promoted
Research & International Scientific Research) of the Ministry of Education,
Science and Culture, the University of Tsukuba Special Research Projects,
and the JSPS Research Fellowships for Young Scientists. ORNL is managed
by Lockheed Martin Energy Research Corporation under contract DE-AC05-
96OR22464 with the U.S. Department of Energy. The MIT group has been
supported by the US Dept. of Energy under the cooperative agreement DE-
FC02-94ER40818. In addition we would like to thank R. Birgeneau, H.Y.
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Fig. 1. The WA98 Experiment at the CERN SPS. This analysis uses the Silicon
Pad Multiplicity Detector (SPMD) and the Photon Multiplicity Detector (PMD) to
measure the charged and neutral multiplicity for each event, and the Mid-Rapidity
Calorimeter (MIRAC) for the measurement of event centrality.
Number of Events
0 100 200 300 400 500 600 700 800 900
Number of Events
Fig. 2. The charged and neutral multiplicity distributions are shown in a) and b).
The open circles represent the minimum-bias distribution. The “central” sample
(ET> 300 GeV) is represented by closed circles for the data and by histograms for
0100 200 300 400 500 600 700 800 900 1000
Fig. 3. This is the scatter plot showing the correlation between Nchand Nγ−like.
The solid outline shows the trend of the minimum bias data. The central sample
(with ET > 300 GeV) is shown as points for the data and as a hatched region for
VENUS (with much lower statistics). Overlaid on the plot are the Z axis and the
DZaxis at a particular value of Z as explained in the text.
Number of Events
Number of Events
-200 -150 -100 -50050100 150 200
Fig. 4. a.) This figure shows the distribution of Z, with the same conventions as in
figure 2. b.) This shows the distribution of DZin the “central” sample for the data
(closed circles) and for VENUS (histogram). The difference in the mean between
the two distributions arises due to the overall scale differences discussed in Section
Number of Events
-15 -10-50510 15
Fig. 5. SZdistribution for the experimental data is shown, overlaid with VENUS
simulations incorporating 0%, 25% and 60% DCC in every event. All of the distri-
butions are normalized to the total number of data events.
ζ(%) Download full-text
DCCs / Central Event
0 10 20 30 40 50 60 70 80 90 100
Fig. 6. 90% C.L upper limit on DCC production per central event as a function of
the fraction of DCC pions under two assumptions. The thick line gives the upper
limit obtained by assuming the σDZin SZ is completely given by the VENUS
calculation requiring to make a cut at 6σ. The dashed line shows a less conservative
limit obtained by using the σDZmeasured in the data itself. This allows us to make
a tighter cut at 5σ, increasing the DCC detection efficiency.