Virtual Observatories A New Era for Astronomy. Reinaldo R. de Carvalho DAS-INPE/MCT 2010
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1 Virtual Observatories
2 Virtual Observatories 1%%&'&$#-&6!&9:#,*3),!#,6!6#$C!&,&$D2 *:#%&+-3;& D&);&-$2!!"! "!" &,&$D2 %),-&,-!"#$%&'&#()*! $#%&!(!!! $ '!%&$ $! (% %)'6!6#$C!;#--&$G $! '!!! $#63#-3),G $! %&$!!! *%#'& E#%-)$ "# #!!#$$ % %& &F0#-3), )E!*-#-& +#&,&'-.'/0&#,&!!!!!"#$%&''&!()#$&*+(#,-)*!!.!!/'0*-&$!1,#'2*3*!3,!-4&!(5((!/)#66!!.!!(71/!89:&$3;&,-#'!(&;3,#$!!.!!<&=!>?!@ABA!!.!!@
3 Dark Energy & Cluster Abundance Sensitivity to Dark Energy equation of state Volume element Comoving distance Huterer & Turner 2001
4 C#'#92!%'0*-&$*!#,6!6#$D!&,&$E2!&F0#-3),!)G!*-#-& Virtual Observatories A New *./01)++ Era for Astronomy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
5 Virtual Observatories
6 A roadmap for finding clusters in the Universe Virtual Observatories de Carvalho et al., JCIS Mass Function Star/Galaxy Separation Comparison to Mock Catalogs DARK ENERGY Translate an image into objects 2DPHOT What is a cluster? Photometric Redshift From Overdensity to Mass Measuring Completeness & Purity VO?
7 Virtual Observatories
8 Virtual Observatories 1 - The Virtual Observatory (VO) concept is the astronomical community s response to the scientific and technological challenges posed by massive and complex data sets. 2 - An underlying concept of the VO is that by providing improved and homogenized data access combined with the tools to manipulate and explore the data, the need for new observations will be reduced even as the scientific output is increased. 3 - VO is not an enterprise driven by a single institute or even one country. It is rather a community endeavor aimed at the democratization of information.
9 Virtual Observatories
10 Significant Increase of Data Virtual Observatories in Astronomy
11 Significant Increase of Data Virtual Observatories in Astronomy What are the Implications? 1 - Investment in Data Grid Network Infrastructure 2 - Development of Fast Data Processing 3 - Synergy with other areas for using specific Data Mining methods VO
12 Virtual Observatories
13 Virtual Observatories
14 Virtual Observatories
15 Star/Galaxy Separation Virtual Observatories Parametric Methods
16 Star/Galaxy Separation Virtual Observatories Machine Learning Methods Parametric Methods Training Set = Attributes + True Classification
17 Star/Galaxy Separation Virtual Observatories Machine Learning Methods Parametric Methods Training Set = Attributes + True Classification Observed Set = Only Attributes Classification based on the rules
18 Star/Galaxy Separation E.C. Vasconcelloset al., PASP Virtual Observatories Machine Learning Methods Parametric Methods Training Set = Attributes + True Classification Observed Set = Only Attributes Classification based on the rules
19 Translate an image into objects 2DPHOT BRAVO Processing of the entire SDSS-DR6 in four bands
20 Virtual Observatories
21 Photometric Redshift Virtual Observatories
22 Virtual Observatories
23 Photometric Redshift Virtual Observatories Coadd galaxy catalog v09.05 and v09.08 High redshift training set: DEEP2
24 Photometric Redshift Virtual Observatories Coadd galaxy catalog v09.05 and v09.08 High redshift training set: DEEP2 What is the context in which these tools are needed? 1 - Access to different types of data Interoperability 2 - Fast Data Processing VO
25 Virtual Observatories
26 What is a Cluster? Virtual Observatories How to Identify a Cluster? 1 - Clusters are not isolated structures time 2 - They evolve dynamically and content 3 - Cluster Galaxies evolve and this might depend on their mass (which mass?) 4 - Error budget when we go from ideal Reinaldo R. de to Carvalho real 5 - How do we handle galaxy evolution?
27 Virtual Observatories
28 How do we Find Clusters? Virtual Observatories
29 How do we Find Clusters? Virtual Observatories SPIDER Spheroidʼs Panchromatic Investigation in Different Environmental Regions
30 Virtual Observatories
31 From Overdensity to Mass Virtual Observatories Lopes et al 2009 Mass = f (Ngal, Lopt, Lx) Relations may change with z
32 Virtual Observatories
33 Comparison to Mock Catalogs Virtual Observatories Selection function Halo Mock true dark matter object with galaxies bounded to its potential well. Cluster (RA, DEC, z) coordinates + list of member galaxies matching Membership? Mtrue-Mobs scatter Raw Completeness Fraction of halos with a cluster counterpart Raw Purity Fraction of clusters corresponding to real halos Completeness C(M,z) Purity P(M,z)
34 Comparison to Mock Catalogs Virtual Observatories Selection function Halo Mock true dark matter object with galaxies bounded to its potential well. Cluster (RA, DEC, z) coordinates + list of member galaxies matching Membership? Mtrue-Mobs scatter Raw Completeness Fraction of halos with a cluster counterpart Raw Purity Fraction of clusters corresponding to real halos Completeness C(M,z) Purity P(M,z) VO needs to bring simulations to the same context as data
35 Virtual Observatories
36 Mass Function and Dark Energy Virtual Observatories
37 Mass Function and Dark Energy Constraints on w-ω m space: Virtual Observatories forecasts Optimistic forecasts for fiducial model: ΛCDM Fixed parameters: σ 8 = 0.8 h = 0.72 Ω x = 1 - Ω m Redshift range: z 1.2 Area: 1000 square degrees Mass threshold: solar masses Contours: Lima&Hu (2004,2005) Fisher Matrix Gray regions: MCMC
38 Virtual Observatories
39 A little Help From VO Virtual Observatories
40 A little Help From VO Virtual Observatories What do we need here? 1 - Tools to interconnect images and catalogs 2 - Development of Fast Data Processing VO
41 A little Help From VO Virtual Observatories Translate an image into objects 2DPHOT What do we need here? Star/Galaxy Separation Photometric Redshift 1 - Tools to interconnect images and catalogs 2 - Development of Fast Data Processing VO What is a cluster? From Overdensity to Mass
42
43 Astronomical Strategies
44 Astronomical Strategies PROBLEM SOLUTION
45 Astronomical Strategies PROBLEM Slow CPU growth SOLUTION Distributed Computing
46 Astronomical Strategies PROBLEM Slow CPU growth Limited storage SOLUTION Distributed Computing Distributed Data
47 Astronomical Strategies PROBLEM Slow CPU growth Limited storage Limited bandwidth SOLUTION Distributed Computing Distributed Data Information Hierarchies
48 Astronomical Strategies PROBLEM Slow CPU growth Limited storage Limited bandwidth SOLUTION Distributed Computing Distributed Data Information Hierarchies - Move only what you need
49 Astronomical Strategies PROBLEM Slow CPU growth Limited storage Limited bandwidth SOLUTION Distributed Computing Distributed Data Information Hierarchies - Move only what you need Data diversity Interoperability
50 Astronomical Strategies PROBLEM Slow CPU growth Limited storage Limited bandwidth SOLUTION Distributed Computing Distributed Data Information Hierarchies - Move only what you need Data diversity Interoperability
51 Astronomical Strategies PROBLEM Slow CPU growth Limited storage Limited bandwidth SOLUTION Distributed Computing Distributed Data Information Hierarchies - Move only what you need Data diversity VO Interoperability
52 Virtual Observatories
53 Strategy for the Future Virtual Observatories Concluding Remarks
54 Strategy for the Future Virtual Observatories Concluding Remarks Translate an image into objects 2DPHOT Star/Galaxy Separation Infrastructure + Data Mining Photometric Redshift From Overdensity to Mass Measuring Completeness & Purity What is a cluster? Comparison to Mock Catalogs IVOA (
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