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WCI 2004 session 3: DATA SORTING

WCI 2004 session 3: DATA SORTING. Can we extract mechanism? Can we extract sources in space-time? What are the differences between p-A an d A-A collisions?. SORTING : Why? Two complementary philosophies. 1- Global overview of data versus collision violence, energy,

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WCI 2004 session 3: DATA SORTING

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  1. WCI 2004 session 3: DATA SORTING Can we extract mechanism? Can we extract sources in space-time? What are the differences between p-A and A-A collisions?

  2. SORTING : Why? Two complementary philosophies • 1- Global overview of data versus collision violence, energy, • global comparison with models : • IPS = geometry (e.g. Mtot,ET,ET12,ZTOT,TKEL…) • IPS do not allow to select mechanisms. • IPS = impact parameter mixing around Fermi energy OR • 2 – Select a given mechanism, an emission source : • Use several global variables, statistical techniques. • « Physics of the sorting » ? Check what you select • Mechanism  impact parameter (large fluctuations E 100 AMeV) SORTING IS DETECTOR DEPENDENT

  3. Impact parameter selector and detector response Central collisions : upper 10% of IPS distributions Correlations between NIMFand IPS is Detector dependent A powerful detector gives closer variances and normalised variances of IMF distributions whatever the IPS (but also lower mean values for IPS based on lcp )  Nc ET  ZMR Zdet  NH ZLCP INDRA Llope et al. PRC 51(1995) 1325

  4. IPS : event mixing IPS = transverse energy Experimental data Doré et al. (INDRA)PLB491(2000) 15 DYWAN simulation (wavelets) Jouault et al. NPA 628(1998) 119 De la Mota &Sébille EPJA 12 (2001) 479 Verify IPS range data/model Treat both in same way

  5. IPS : Disentangle QP and MR emissions Get proportions and properties of both types of emissions (sources  ?), which may differently depend on b See Olmi Most peripheral collisions b0.6 bmax. IPS=TKEL Results depend on assumption for QP emission (isotropic or not)

  6. Coulomb proximity decay: evaporation from PLF  Hudan et al. Nucl-ex/0308031 Simulation : inv in j for emitting j from PLF parameterised as j(E) = R2fj(1-Uc/E) Modified Uc=ZfZj/Rfj + ZTLFZj/RTLFj + ZTLFZf/RTLFf - ZTLFZ/RTLFPLF Lower B  emission favoured between PLF and TLF in early emission : RTLFPLF =30-70 fm(t250 fm/c) Data 50 AMeV Cd+Mo:  from PLF (E22 MeV) Including early emission increases APLF and * from 2.3 to 4 MeV. Influence on mid-rapidity « source ».

  7. Data sorting: Identify mechanisms use complete events (QP or single source) see M. Bruno, Srivastava, INDRA Peripheral collisions select events with minimum MR emission : velocity, momentum criteria (Bruno, Bougault) Charge density (INDRA) Remove preequilibrium Au+C (Srivastava) • Central collisions: • Complete events and event shape (flow) (Bruno, INDRA) • Multidimensional analyses (INDRA)

  8. Data sorting: from pure binaryevents to single source 2.5mb 0.7 mb Parallel velocity Tool: charge density vs c.m. velocity along event axis 36Ar+58Ni 95 AMeV Complete events: 80% of total charge and linear momentum detected (semi-central and central collisions) - 145 mb (5.2% R) E. Galichet PhD thesis, and NIM A 441 (2000) 517

  9. Data sorting: selection of central events Au+C, Au + Cu, Au + Au Well detected >90% Ap+T and spherical events θflow> 60o Some percent of the measured cross section ISOTROPY Nucl. Phys. A 724 (2003) 455

  10. Selection of compact single source: complete events and flow angle INDRA J.D. Frankland et al. NPA 689 (2001) Flow angle Calculated with Fragments only (Z  5) lcp properties show evolution from binary to fusion collisions 

  11. Sorting :Statistical techniques • Work in multidimensional space • Project on a discriminant plane, or axis Principal Component Analysis Chimera variable Discriminant Analysis Neuronal Network Enlarge the samples Their properties must be carefully verified INDRA central collisions See N. Le Neindre

  12. Data sorting is equivalent to creating a statistical ensemble Characterize The pertinent variables The type of statistical ensemble See Francesca Gulminelli

  13. Space-time extent of sources Can we disentangle space and time ? Interferometry New technique to partly avoid space and time mixing Imaging (B. Lynch) Velocity correlations (lcp-IMF, IMF-IMF) (Geraci, De Souza, Natowitz)

  14. BillLynch

  15. p-A versus A-A Nautilus Ar+Au & Kr+Au Similarities and differences between reactions F-F emission time vs E* (Beaulieu PRL84(2000)5971) Similar above 4-5 AMeV : Multifragmentation region

  16. In p-A reactions • single source – no neck emission • simplest case for thermal effects • negligible deformation, angular momentum effects • formation of hot residue in a dilute state • no compression • entropy per nucleon reaches maximum very rapidly • residues formed over full range of E* with one beam • limited maximum E*/A due to transparency effects V. Viola See Karnaukhov

  17. MF: more or less compression in central HI collisions Depends on entrance channel asymmetry 2 systems, 1th(samepartitions) Different KE of fragments 1 asymmetric system, 2th. Same KE of fragments Bellaize et al. (INDRA) NPA709 (2002) 367

  18. p-A and A-A at high excitation Zs=59 th 6.5MeV MIMF /ZS =0.065 KE : Coulomb Beaulieu et al (ISiS) PRC64 (2001) Zs=68th 6.2 MeV MIMF /ZS 0.10 th and ZS from SMM (backtr) Zs=75th 6.5 MeV MIMF /ZS 0.095 Bellaize et al. (INDRA) O. Lopez (INDRA) In p-A vs central A-A for th 6. MeV : less fragments, and similar fragment KE. Uncertainties on th ? On ZS? Detector efficiencies on MIMF

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