diffusion of conserved-charge fluctuations
TRANSCRIPT
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Masakiyo Kitazawa(Osaka U.)
Diffusion of Conserved-ChargeFluctuations
MK, Asakawa, Ono, Phys. Lett. B728, 386-392 (2014)Sakaida, Asakawa, MK, PRC90, 064911 (2014)MK, arXiv:1505.04349, Nucl. Phys. A942, 65 (2015)
Workshop on Fluctuation, GSI, 4/Nov./2015
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Objective 1: ∆η Dependence @ ALICE
ALICEPRL 2013
rapidity window
large
small
∆η dependentthermometer?
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Objective 2
Theories
Experiments• final state fluctuation• fluctuation in a pseudo rapidity
• fluctuation in a spatial volume• equilibration (in an early time)
Detector
on the basis of statistical mechanics
What are their relation?
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Time Evolution of Fluctuations
Diffusion modifies distribution
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Time Evolution of Fluctuations
Detector(momentum-space)
rapidity
conversion ηy at kinetic f.o.“Thermal Bluriing” modifies distribution
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revisitingSlot Machine Analogy
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An Exercise (Old Ver.).Rconstructing Baryon Number Cumulants
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Slot Machine Analogy
N
P (N)
N
P (N)
= +
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Extreme Examples
N
Fixed # of coins
N
Constant probabilities
N N
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Reconstructing Total Coin Number
P (N )= P (N )B1/2(N ;N )
MK, Asakawa, 2012
MK, Asakawa, 2012Bzdak, Koch, 2012, 2015Luo, 2012, 2015, …
Application to efficiency correction:CaveatWe still have 50%efficiency loss in proton #
binomial distribution
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Difference btw Baryon and Proton Numbers
deviates from the equilibrium value.(1)
(2) Boltzmann (Poisson) distribution for
For free gas
genuine info. noise (Poisson)
MK, Asakawa, 2012
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STAR, QM2015
Clear suppression!ex. Asakawa, Ejiri, MK, 2009
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Main PartDiffusion of conserved-charge fluctuations
and rapidity blurring
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2 Slot Machines
Can we get the cumulants of single slot machine?
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2 Slot Machines
Can we get the cumulants of single slot machine?
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Many Slot Machines
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Many Slot Machines
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Many Slot Machines
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Many Slot Machines
: Poisson
: cumulants of
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Many Slot Machines
: Poisson
: cumulants of
Koch, 0810.2520
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Time Evolution of Fluctuations
Detector
(momentum-space)rapidity
Diffusion modifies distribution Asakawa, Heinz, Muller, 2000
Jeon, Koch, 2000Shuryak, Stephanov, 2001
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Thermal Blurring
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Thermal Blurring
coordinate-space rapidityof medium
momentum-space rapidityof medium
coordinate-space rapidity of individual particles
Under Bjorken picture,
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Thermal distribution in η space
pion
nucleon• pions• nucleons
Y. Ohnishi+in preparation
• blast wave• flat freezeout surface
Blast wave squeezes the distribution in rapidity space
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Thermal distribution in η space
• pions• nucleons
half-
wid
th(∆
y)Y. Ohnishi+in preparation
• blast wave• flat freezeout surface
kurto
sis
Rapidity distribution is not far away from Gaussian.
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Formalism
Detector
Particles arrive at the detector with some probability. Sum all of them up. Make the distribution. Take the continuum limit.
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∆η Dependence
With vanishing fluctuationsbefore thermal blurring
Cumulants after blurringtake nonzero values
With ∆y=1, the effect isnot well suppressed
Cumulants after blurring
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Centrality Dependence
Is the centrality dependence understood solely by the thermal blurring at kinetic f.o.?
More central lower Tlarger β
Weakerblurring
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Centrality Dependence
Assumptions:• Centrality independent
cumulant at kinetic f.o.• Thermal blurring at
kinetic f.o.
Centrality dep. of blast wave parameters can qualitatively describe the one of
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Diffusion (+ Thermal Blurring),of Non-Gaussian Cumulants
theseprocesses
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<δNB2> and < δNp
2 > @ LHC ?
should have different ∆η dependence.
Baryon # cumulants are experimentally observable! MK, Asakawa, 2012
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<δNQ4> @ LHC ?
suppression
How does behave as a function of ∆η?
enhancementor
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<δNQ4> @ LHC ?
suppression
How does behave as a function of ∆η?
enhancementor
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Hydrodynamic Fluctuations
Stochastic diffusion equation
Landau, Lifshitz, Statistical Mechaniqs IIKapusta, Muller, Stephanov, 2012
Markov (white noise)
Gaussian noiseFluctuation of n is Gaussian in equilibrium
+continuity
cf) Gardiner, “Stochastic Methods”
Shuryak, Stephanov, 2001
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Diffusion Master Equation
Divide spatial coordinate into discrete cells
probability
MK, Asakawa, Ono, 2014MK, 2015
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Diffusion Master Equation
Divide spatial coordinate into discrete cells
Master Equation for P(n)
probability
Solve the DME exactly, and take a0 limit
No approx., ex. van Kampen’s system size expansion
MK, Asakawa, Ono, 2014MK, 2015
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A Brownian Particle’s Model
Hadronization (specific initial condition)
Freezeout
Tim
e ev
olut
ion
Initial distribution + motion of each particle cumulants of particle # in ∆η
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A Brownian Particle’s Model
Hadronization (specific initial condition)
Freezeout
Tim
e ev
olut
ion
Initial distribution + motion of each particle cumulants of particle # in ∆η
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Diffusion + Thermal Blurring Hadronization
Kinetic f.o. (coordinate space)
Kinetic f.o. (momentum space)
Diffusion + thermal blurring = described by a single P(x) Both are consistent with Gaussian Single Gaussian
diffusion
blurring
Total diffusion:
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Baryons in Hadronic Phase ha
dron
ize
chem
. f.o
.
kine
tic f.
o.10~20fm
mesonsbaryons
time
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Time Evolution in Hadronic Phase Hadronization (initial condition)
Boost invariance / infinitely long system Local equilibration / local correlation
suppression owing tolocal charge conservation
strongly dependent onhadronization mechanism
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Time Evolution in Hadronic Phase Hadronization (initial condition)
Detector
Diff
usio
n +
Blu
rrin
g
suppression owing tolocal charge conservation
strongly dependent onhadronization mechanism
Boost invariance / infinitely long system Local equilibration / local correlation
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∆η Dependence: 4th order Initial Condition
Characteristic ∆η dependences!
MK, NPA (2015)
new normalization
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∆η Dependence: 4th order
at ALICEat ALICE
Initial Condition
baryon #
MK, NPA (2015)
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∆η Dependence: 3rd order Initial Condition
MK, NPA (2015)
at ALICEat ALICEbaryon #
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4th order : Large Initial Fluc.
at ALICEat ALICEbaryon #
Initial Condition
MK, NPA (2015)
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∆η Dependence @ STAR
Non-monotonic dependence on ∆y ?
X. Luo, CPOD2014
MK, 2015
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Summary
Plenty of information in ∆ηdependences of various cumulants
primordial thermodynamicsnon-thermal and transport propertyeffect of thermal blurring
and those of non-conserved charges, mixed cumulants…
With ∆η dep. we can explore
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Future Studies
Experimental side: rapidity window dependences baryon number cumulants BES for SPS- to LHC-energies
Theoretical side: rapidity window dependences in dynamical models description of non-equilibrium non-Gaussianity accurate measurements on the lattice
Both sides: Compare theory and experiment carefully Let’s accelerate our understanding on fluctuations!
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Very Low Energy Collisions
Large contribution of global charge conservationViolation of Bjorken scaling
Fluctuations at low √s should be interpreted carefully!Comparison with statistical mechanics would not make sense…
detector
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How to Introduce Non-Gaussianity?
Stochastic diffusion equation
Choices to introduce non-Gaussianity in equil.: n dependence of diffusion constant D(n) colored noise discretization of n
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How to Introduce Non-Gaussianity?
Stochastic diffusion equation
Choices to introduce non-Gaussianity in equil.: n dependence of diffusion constant D(n) colored noise discretization of n our choice
Fluctuations measured in HIC are almost Poissonian.REMARK:
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Time Evolution of Fluctuations
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Time Evolution of Fluctuations
detector
① continues to change until kinetic freezeout due to diffusion.
② changes due to a conversion y ηat kinetic freezeout
“Thermal Blurring”
Particle # in ∆η