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Computational Data Analysis and Simulation in Cosmology: The LAC Experience
Outline
1. Computational Cosmology Scope
2. Expertise, Collaborative Work and Team
2. The LAC Cosmology Experience
4. Data Analysis (Virgo Consortium DATA)
SeminárioDivisão de Astrofísica
23/3/2010
R.R.Rosa, F.M. Ramos, C.A. Caretta, H.F.C. Velho Computer Physics Communications180(4): 621-624, 2009
Reinaldo R. RosaGRUPO DE FÍSICA COMPUTACIONAL E COMPUTAÇÃO CIENTÍFICA
Laboratório Associado de Computação e Matemática Aplicada (LAC)Centro de Tecnologias Especiais (CTE) – INPE – São José dos Campos-SP-Brazil
1. Escopo da Cosmologia Computacional
Observação+Teoria+Simulação Visualização, Análise e Animação
Computação Aplicada
(a) (b) (d)
(d)
(a) QCDSP (b) 12,3 k, 11 Tflops (c) RHIC, (d) SimulaçãoLaboratório Nacional de Brookhaven (Upton, EUA)http://www.bnl.gov/rhic/
Colisor de Ions Relativísticos Pesados (RHIC)
?
(0,∞ ) (10 -35,1028) (10-32,1027) (10 -6,1013 ) (180s, 108 ) (380mil, 3000) (200M, 30) (10B, 4) (14B,3)
1. Computational Cosmology Scope
Observação+Teoria+Simulação Visualização, Análise e Animação
Computação Aplicada
2. Expertise, Collaborative Work and Team
GRUPO DE FÍSICA COMPUTACIONAL E COMPUTAÇÃO CIENTÍFICA - LAC
Computação Aplicada ao Estudo da Formação de Padrões Espaço-Temporais
E.N. MacauF. M. RamosH. F.C. VelhoJ.S. TravelhoN.L.VijaykumarR.R.Rosa
Gustavo ZaniboniMurilo DantasRamon FreitasThalita Veronese
Cristiano StriederCristiane Pires CamiloEduardo CharlesStephanie Liles
Alex Wuensche (INPE)
Ana P. Andrade (UESC)
Andre Ribeiro (UESC)
Anne De Wit (ULB)
Antonio F. da Silva (UFBA)
Arcilan Assireu (EFEI)
Adriana Mattedi (EFEI)
Armando Bernui (EFEI)
Cesar Caretta (UG)
Fernando Oliveira (UnB)
German Gomero (UESC)
Hanumant Sawant (INPE)
Harry Swinney (UTA)
Henrique Oliveira (UERJ)
Hugo V. Capelato (INPE)
José Pontes (UFRJ)
Marian Karlick (Ondrejov)
Mariana Baroni (UFABC)
Marcelo Rebouças (CBPF)
Martin Makler (CBPF)
Patricio Letelier (UNICAMP)
Reinaldo Carvalho (INPE)
Surjalal Sharma (UMD)
Wiiliam Hipolito (UFBA)
Colaboradores:
2. Os trabalhos em Cosmologia no LAC
2000-2002: A. Wuensche, A.L. Ribeiro (Physica D)
2003-2004: C.P. Camilo (DM), II Nova Física no Espaço
2004-2005: M. Makler (II WSASC), C. Caretta (PCI)
2005-2006: A.P.Andrade (Physica D)
2007-2009: Mineração e Análise de Dados (Virgo)(A&A, CPC)
2010: Millenium, West Ontario, Dark Energy Survey, WMAP7, Modelo 2 CSF (R. Rosenfeld, D. Bazeia)
“Extended Singularity”
•Domain walls•Strings•Monopoles•Textures
£, V(φ,χ)
2-field models?
∆∆
≈∆∆
aTD
aTD
aC
aC
gg
bgg
10,
27,
10,
27,
f(χ)
Navier-Stokes
Demandas para as Abordagens de Campos Escalares e Defeitos Topológicos
2CSF
Extreme Event Dynamics and Chaotic Advection in the Formation ofGalaxy-Sized Dark Matter Structures
Reinaldo R. RosaLab Associado de Computação e Matemática Aplicada (LAC)
[email protected] / [email protected]
Outline
1. Data and Motivation from Previous Results2. The Physics of Extreme Events (Xevents)3. Data Analysis and Results4. Conclusion Remarks
IX Nova Física no Espaço28/2 -05/3/2010
Campos do Jordão
R.R.Rosa, F.M. Ramos, C.A. Caretta, H.F.C. Velho Computer Physics Communications180(4): 621-624, 2009
DATA: http://www.virgo.dur.ac.uk/
E. Bertschinger, Ann. Rev. Astron. Astrophys, 1998, 36:599-654Simulations of structure formation in the Universe
Physics + simulation algorithms (BHTA, P3M, PP)
Data Mining Algorithms: FoF, DENMAX, HFoF, etc.
DATA: The Virgo consortium
Lambda CDM L=Box size= 239.5 Mpc/h 2563 particlesΩ = 0.3,
ΩΛ = 0.7 (gravitational instability + pressure)H0 = 70 km/(Mpc sec)Mass per particle 6.86x1010Mo/h
L/3
AP3M 02 Cray T3D PCC Edinburgh Max Planck Garching.
Data Mining FoFCaretta, Rosa, Velho, Ramos e Makler, A&A, 487(2):445-451, 2008
PREVIOUS ANALYSIS AND RESULTS (NF 2005/2008)
(10 ≤ Z ≤ 0)
5 1.5 0
Gradient Pattern Analysis Gradient Asymmetry Coefficient :
GA ≡ (ε – g) / g
g= amount of asymmetric fluctuations (vectors in ∇G)
ε= energy of geometric correlation among fluctuations(Rosa et al., Int. J. Mod. Phys. C, 10(1)(1999):147.
Rosa et al.; Braz. J. Phys. 33 (2003):605Assireu et al., Physica D, 2002)
g ε
Andrade, A.P.; Ribeiro A.L.; Rosa, R.R., Physica D 223:139-145, 2006
GA (%)Random Patterns: > 90%Typical range for DNS Turbulence Patterns (800<Re<1000): 61-86%Quasi-Symmetric Patterns: 1-40%
Andrade, A.P.; Ribeiro A.L.; Rosa, R.R., Physica D 223:139-145, 2006
-5/3
k (Mpc-1)
U (1
0+53 J
)
Caretta, Rosa, Velho, Ramos e Makler, A&A, 487, Issue 2, 2008, pp.445-451
H. Aref, 2002 The development of chaotic advection. Physics of Fluids 14, 1315-1325.
⟨V⟩ = µ - (σ/ξ) + (σ/ξ)Γ(1-ξ)
Lagrangian Turbulence (Chaotic Advection)
Ex. Nonlinear Wind Flow Clouds as Nondissipative tracers with
µ: location parameter σ: scale parameter (dispersion)ξ: shape parameter
[P (nmax) = nmax / Nc] X (NT/nmx)
How to represent the distribution of the maximum level of a variable in a particular domain interval if we have the list of maximum values for a meaningful collection. It is useful in characterizing (or predicting) the frequency (or chance) that an extreme value will be localized (occur).
The Fisher–Tippet theorem (extreme value theorem):The maximum of a sample of a correlated random variable after proper renormalization converges in distribution to one of 3 possible distributions: Gumbel, Fréchet or Weibull distribution.
Extreme Value Approach
NTNc
nmax > n0
• Lumps of energy: extreme eventsextreme events
Intense and rare events described by the tailstails of probability distributions
Extreme Event TheoryExtreme Event Theory: the limiting cumulative distribution of maxima of sequences of random variables is given by the Generalized Extreme ValueGeneralized Extreme Value (GEV) distribution:
Shape parameter
Scale parameter
Location parameter
GEV for GxSH, z = 0, 1.5 and 5.0P
n
Total Energy (normalized) = (1/n) x N
•Light travels from one point to another along a path that minimizes the time of travel.•The “principle of least action” minimizes action.•Thermodynamical potentials are extremized (maximum entropy, minimum energy)•Atractors optimizes the phase space dynamics•The fluid maximizes the tracers momenta•The universe maximizes its local density (gravitational instability?) and minimizes its global density (expansion, dark energy?)
Xevents: some physical quantity is minimized or maximized extremized
Concluding Remarks
1- Evidence of Non-gaussian density fluctuations
from Lambda-CDM
2- Baryonic matter as tracers of dark matter?
3- How it works for Gx collisions? (movies)
4- Reference Xevent modelling from
cosmological statisticalGEV parameters
Acknowledgments:
Data:Max Planck Institute: http://www.mpa-garching.mpg.de/Virgo
Movies:John DubinskiDepartment of Astronomy & AstrophysicsUniversity of Toronto
Supercomputer Support:CPTEC-INPE
“The more is different!!”Philip Anderson
E as diferenças resultam da Diversidade Estrutural...
• Obrigado!
August 23-26, 2010São José dos Campos, SP, Brazilhttp://www.lac.inpe.br/CCIS/
1st Conference of
Computational
Interdisciplinary
Sciences