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

reinaldo@lac.inpe.br / reinaldo.rosa@pq.cnpq.br

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)

reinaldo@lac.inpe.br / reinaldo.rosa@pq.cnpq.br

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