Dynamical Modeling of Relativistic Heavy Ion Collisions Tetsufumi Hirano Workshop at RCNP, Nov 4, 2004 Work in partly collaboration with Y.Nara (Frankfurt),

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Presentation transcript:

Dynamical Modeling of Relativistic Heavy Ion Collisions Tetsufumi Hirano Workshop at RCNP, Nov 4, 2004 Work in partly collaboration with Y.Nara (Frankfurt), M.Gyulassy (Columbia)

…suggest appealing QGP-based picture of RHIC collision evolution, BUT invoke 5 distinct models, each with own ambigu- ities, to get there. pQCD parton E loss The Five Pillars of RHIC Wisdom Ideal hydro Quark recombination  constituent q d.o.f. CGC Statistical model Early thermalization + soft EOS Very high inferred initial gluon density Very high anticipated initial gluon density u, d, s equilibration near T crit Slide from T.Hallman ~STAR white paper

The State of RHIC Theory LQCD: CPU limitations; applic’y to dynamic matter? Statistical model: equilib’n or phase space? Hydro:  0, freezeout, boost- invariance ambigs. Quark recomb.: predictive power? Parton E loss: untested assumptions Gluon saturation: universal scale estab- lished? Emerging description of beautiful evolution from one new state of matter to another! And Yet, A patchwork, with model parameters adjusted independ- ently for each element In order to rely on theory for compelling QGP discovery claim, we need: greater coherence; fewer adjusted parameters; quantitative estimates of theoretical uncertainties Slide from T.Hallman ~STAR white paper

Hairsplitting Comments from Our Approach How are these consistent with each other? Discussion from hydrodynamic point of view: 1. Hydro vs. Statistical model (main topic) 2. Hydro vs. Recombination model 3. Hydro vs. Jet tomography 4. Hydro vs. CGC These discussions will tell us what to do next and lead to a unified understanding of HIC.

Today’s Bad News The elliptic flow at RHIC is “accidentally” reproduced by a hydro model.

Hydro vs. Statistical Model (1) Chemical parameters  particle ratio Thermal parameters  p t spectra Statistical model T ch >T th (conventional) hydro T ch =T th No reproduction of ratio and spectra simultaneously

Hydro vs. Statistical Model (2) P.Huovinen, QM2002 proceedings

Hydro vs. Statistical Model (3) ii Introduction of chemical potential for each hadron! Single T f in hydro Hydro works? Both ratio and spectra?

Hydro vs. Statistical Model (4) EOS Example of chem. potential Partial chemical equilibrium (PCE) Expansion dynamics is changed (or not)? T.H. and K.Tsuda(’02) 

Hydro vs. Statistical Model (5) Model PCE Model CE Contour(T=const.) T(  ) at origin T.H. and K.Tsuda(’02) (T th ) 

Hydro vs. Statistical Model (6) How to fix T th in conventional hydro Response to p T slope Spectrum harder as decrease T th Up to how large p T ? T th independence of slope in chemically frozen hydro No way to fix T th Suggests necessity of (semi)hard components Charged hadrons in AuAu 130GeV

Hydro vs. Statistical Model (7) Chemical Equilibrium Partial Chemical Equilibrium  K p T.H. and K.Tsuda (’02) Kolb and Heinz(’04) Is v 2 (p T ) sensitive to the late dynamics?

Hydro vs. Statistical Model (8) Slope of v 2 (p T ) ~ v 2 / Response to decreasing T th (or increasing  ) v2v2 <pT><pT>v 2 / CE PCE Generic feature! pdV work + (number) /(entropy)   

Hydro vs. Statistical Model (9) Simplest case: Pion gas Longitudinal expansion  pdV work! dE T /dy should decrease with decreasing T th.  dN/dy should so. CFO: dS/dy = const.  dN/dy = const.  decreases CE: dS/dy = const.  dN/dy decreases (mass effect)  can increase as long as dN/dy decreases.

Hydro vs. Statistical Model (10) PHENIX white paper, nucl-ex/

Hydro vs. Statistical Model (11) Choice of T th in conventional hydro results from neglecting chemical f.o. The great cost one has to pay for “simplification”! Importance of chemical potential for each hadrons within hydrodynamics “No-Go theorem”. Yet you use? >90% hydro results at SPS and RHIC do not make sense! Chemical eq. mimics viscous hydro?

Today’s Good News Currently, hydro+cascade is the only model which reproduces the elliptic flow, particle ratio, and particle spectra. Caveat: Need realistic interface and oversampling to get rid of numerical artifacts. D.Teaney et al., nucl-th/

Hydro vs. Recombination (1) R.J.Fries et al. (’03) T c =175MeV & v T = 0.55??? reco(Duke) T.H. and K.Tsuda (’02) Half of radial flow comes from hadron phase in hydro Parameter dependence? Today, I won’t discuss (violation of) energy conservation, decrease of entropy…

Hydro vs. Recombination (2) Soft+hard reco is important? Naïve idea: Hydro+jet model with recombination via string fragmentation PHENIX “model killer” plot! nucl-ex/ Pick up a parton from QGP Only mass effect T.H.,QM2004 Associated yield 1.7<pT<2.5GeV/c

Hydro vs. Jet Tomography (1) I.Vitev, nucl-th/ Input: R AA Output: T.H. and Y.Nara (’04) Input: dN ch /d  Output: consistent?

Hydro vs. Jet Tomography (2) Jet tomography: “Color charge density” Hydrodynamics: Parton density cf.) Parton density in chem. eq. Not complete chem. eq.!  Need chemical non-eq. description rate eq. for n g and n q (N f =3), (N f =2) > <

Hydro vs. CGC (1) Kharzeev and Levin (’01) Gluons produced from two CGC collisions (KLN) E T /N ~ 1.6 GeV  Consistent with classical Yang Mills on 2D lattice (KNV, Lappi)  Inconsistent with exp. data ~0.6GeV T.H. and Y.Nara(’04)

Hydro vs. CGC (2) Gluons produced from two CGC collisions (KLN) E T /N ~ 1.6 GeV Initial condition of hydrodynamic simulations E T /N ~ 1.0 GeVE T /N ~ 0.55 GeV  Consistent with classical Yang Mills on 2D lattice (KNV)  Consistent with exp. data ~0.6 GeV Final (psuedo)rapidity spectra of all hadrons This should be obtained through non-equilibrium processes.  Production of entropy Hydrodynamic evolution  “PdV work” reduces E T /N.

Hydro vs. CGC (3) Need a mechanism to reduce E T /N ? E T and/or N Non-equilibrium description is extremely important. Can we get a short thermalization time (~1fm/c)? Is Boltzmann (elastic+inelastic) sufficient for this? If not, may we need non-eq. quantum field approaches?

Summary so far We should keep in mind in modeling of HIC: 1.“The right model in the right place” basis Time scale Energy/momentum scale 2.Consistency among models 3.Treatment of interface among models 4.The number of parameters/assumptions as small as possible