This document discusses using Python to explore the mysteries of the universe. It introduces cosmological and astrophysical concepts like the cosmic star formation rate and supermassive black holes. It presents the PyCosmicStar code for modeling the cosmic star formation rate using different dark matter halo mass functions. Wavelet coherence analysis is also demonstrated for studying connections between signals like the sun and Earth.
Do Cosmos a Terra: Usando Python para desvendar os mistérios do Universo.
1. Do Cosmos a Terra: Usando Python para
desvendar os mist´erios do Universo.
Dr. Eduardo S. Pereira1
1Instituto Nacional de Pesquisas Espaciais
Divis˜ao de Astrof´ısica
Dispon´ıvel em: http://pt.slideshare.net/duducosmos
09/Novembro/2015
2. Sum´ario
1 Introduc¸ ˜ao
2 Cosmos, ou A Evoluc¸ ˜ao do Universo.
3 Um pouco mais de Cosmologia e Astrof´ısica
4 PyCosmicStar
5 A Taxa C´osmica de Formac¸ ˜ao Estelar e Os Buracos Negros
Supermassivos
6 Sol e Terra
7 Fim –*.*–
8 Referˆencias Bibliogr´aficas
12. Um pouco mais de Cosmologia e Astrof´ısica
O Formalismo Tipo Press-Schechter
13. Um pouco mais de Cosmologia e Astrof´ısica
O Modelo de Formac¸ ˜ao Estelar
Halos de mat´eria escura s˜ao poc¸os de potencial gravitacional;
Se o halo tiver massa maior que um certo limiar a formac¸ ˜ao
estelar ir´a ocorrer;
Os primeiros halos capazes de formar estrelas seriam formados
em z ∼ 20 com massa da ordem de 106M
[Salvadori, Schneider e Ferrara 2007]
14. Um pouco mais de Cosmologia e Astrof´ısica
O Modelo de Formac¸ ˜ao Estelar
Halos de mat´eria escura s˜ao poc¸os de potencial gravitacional;
Se o halo tiver massa maior que um certo limiar a formac¸ ˜ao
estelar ir´a ocorrer;
Os primeiros halos capazes de formar estrelas seriam formados
em z ∼ 20 com massa da ordem de 106M
[Salvadori, Schneider e Ferrara 2007]
15. Um pouco mais de Cosmologia e Astrof´ısica
O Modelo de Formac¸ ˜ao Estelar
Halos de mat´eria escura s˜ao poc¸os de potencial gravitacional;
Se o halo tiver massa maior que um certo limiar a formac¸ ˜ao
estelar ir´a ocorrer;
Os primeiros halos capazes de formar estrelas seriam formados
em z ∼ 20 com massa da ordem de 106M
[Salvadori, Schneider e Ferrara 2007]
18. PyCosmicStar
O c´odigo
from pycosmicstar.lcdmcosmology import lcdmcosmology
import matplotlib.pyplot as plt
# I n s t a n c i n g a LCDM Object .
lcdmUniverser = lcdmcosmology(omegam=0.24,omegab=0.04,
omegal=0.73,h=0.7)
z = arange(0, 10.5, 0.1)
# The age of the Universe as a f u n c t i o n of the r e d s h i f t
plt.plot(z, [lcdmUniverser.age(zi) for zi in z])
plt.xlabel(r"$z$ - Redshift")
plt.ylabel(r"$t$ (yr)")
plt.show()
20. PyCosmicStar
O c´odigo
from pycosmicstar.cosmicstarformation import cosmicstarformation
from pycosmicstar.lcdmcosmology import lcdmcosmology
from pycosmicstar.observationalCSFR import ObservationalCSFR
import matplotlib.pyplot as plt
from numpy import arange , array
z = arange(0, 20, 0.1)
#Cosmic Star Formation Rate using
# Tinker e t al . dark haloes mass f u n c t i o n
myCSFR_TK = cosmicstarformation(cosmology=lcdmcosmology ,
massFunctionType="TK",
delta_halo =200)
21. PyCosmicStar
O c´odigo
#Cosmic Star Formation Rate using
# Press and Schechter dark haloes mass f u n c t i o n
myCSFR_PS = cosmicstarformation(cosmology=lcdmcosmology ,
massFunctionType="PS")
#Cosmic Star Formation Rate using
# Seth e t al . dark haloes mass f u n c t i o n
myCSFR_ST = cosmicstarformation(cosmology=lcdmcosmology)
22. PyCosmicStar
O c´odigo
csfrTK = array([myCSFR_TK.cosmicStarFormationRate(zi) for zi in z])
csfrPS = array([myCSFR_PS.cosmicStarFormationRate(zi) for zi in z])
csfrST = array([myCSFR_ST.cosmicStarFormationRate(zi) for zi in z])
31. Sol e Terra
Coerencia Wavelet
import numpy as np
from piwavelet import piwavelet
# Generation of the Random Signal 1
y1 = np.random.rand(100)
# Generation of the Random Signal 2
y2 = np.random.rand(100)
# Time s t e p
x = np.arange(0,100,1)
# Normalization of the Signal 1
y1 = (y1-y1.mean())/y1.std()
# Normalization of the Signal 2
y2 = (y2-y2.mean())/y2.std()
# Wavelet Coherence A n a l y s i s
myCoherence = piwavelet.wcoherence(y1,y2)
32. Sol e Terra
Coerencia Wavelet
# Plot of the Coherence Map
myCoherence.plot(t = x, title=’Test’,units=’sec’)
# I f you want to know the i n d i v i d u a l p r o p e r t i e s .
Rsq ,period ,scale ,coi ,sig95=myCoherence()
36. Fim –*.*–
Obrigado
Star Formation Dreams- ´Oleo sobre
tela. Em andamento.
http:
//pereirasomozartgallery.
edupereira.webfactional.com/
https://www.facebook.com/
pereirasomozagallery/
37. Referˆencias Bibliogr´aficas
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v. 487, p. 704, out. 1997.
DAIGNE, F. et al. Hierarchical growth and satr formation:
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ago. 2006.
HOPKINS, A. M. On the evolution of star-forming galaxies. APJ,
American Physical Society, v. 615, p. 209–221, nov. 2004.
HOPKINS, P. F.; RICHARDS, G. T.; HERNQUIST, L. An
observational determination of the bolometric quasar luminosity
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JENKINS, A. et al. The mass function of dark matter haloes. Mon.
Not. R. Astron. Soc., v. 321, p. 372–384, 2001.
38. Referˆencias Bibliogr´aficas
PEREIRA, E. dos S.; MIRANDA, O. D. The role of the dark matter
haloes on the cosmic star formation rate. New Ast., v. 41, p. 48–52,
2015.
PEREIRA, E. S.; MIRANDA, O. D. Stochastic background of
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v. 401, p. 1924–1932, jan. 2010.
PEREIRA, E. S.; MIRANDA, O. D. Supermassive black holes:
connecting the growth to the cosmic star formation rate. MNRAS
Letters, v. 418, p. L30–L34., 2011.
PRESS, W. H.; SCHECHTER, P. Formation of galaxies and
clusters of galaxies by self-similar gravitational condesation. Apj, p.
425–438, fev. 1974.
SALPETER, E. E. The luminousity function and stellar evolution.
Apj, v. 121, p. 161–167, 1955.
39. Referˆencias Bibliogr´aficas
SALVADORI, S.; SCHNEIDER, R.; FERRARA, A. Cosmic stellar
relics in the galactic halo. MNRAS, v. 381, p. 647–662, out. 2007.
SCALO, J. M. The stellar initial mass function. Fundamentals
Cosmic Phys., v. 11, p. 1–278, maio 1986.
SHETH, R. K.; MO, H. J.; TORMEN, G. Ellipsoidal collapse and an
improved model for the number and spatial distribuition of dark
matter haloes. Mon. Not. R. Astron, v. 323, p. 1–12, set. 2001.